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Found 372 papers, 192 papers with code
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis
Furthermore, to combine syntactic structure and semantic information, we equip the attention score matrices by syntactic mask matrices.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
TL-Explorer: A Digital Humanities Tool for Mapping and Analyzing Translated Literature
TL-Explorer is a digital humanities tool for mapping and analyzing translated literature, encompassing the World Map and the Translation Dashboard.
Region Graph Embedding Network for Zero-Shot Learning
To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.
{\mathcal{P}^2}: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation
Text verbalization of knowledge graphs is an important problem with wide application to natural language generation (NLG) systems.
MovieChats: Chat like Humans in a Closed Domain
In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.
Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
However, current PKT studies primarily focus on the implicit relationship between code content and knowledge assessment, often overlooking two types of noise signals in long-term programming activities: unwanted signals from unrelated submissions and weak signals from minor modifications.
AuthGuard: Generalizable Deepfake Detection via Language Guidance
To achieve this, we train an expert deepfake vision encoder by combining discriminative classification with image-text contrastive learning, where the text is generated by generalist MLLMs using few-shot prompting.
Projection Pursuit Density Ratio Estimation
Density ratio estimation (DRE) is a paramount task in machine learning, for its broad applications across multiple domains, such as covariate shift adaptation, causal inference, independence tests and beyond.
MGS3: A Multi-Granularity Self-Supervised Code Search Framework
Subsequently, we introduce a novel Multi-Granularity Self-Supervised contrastive learning code Search framework (MGS$^{3}$}).
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression
Large Language Models (LLMs) have enabled remarkable progress in natural language processing, yet their high computational and memory demands pose challenges for deployment in resource-constrained environments.
Learning to Select In-Context Demonstration Preferred by Large Language Model
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks during inference using only a few demonstrations.
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression
Text-to-Image (T2I) models have demonstrated impressive capabilities in generating high-quality and diverse visual content from natural language prompts.
Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models
In this study, we investigate low-rank tensorized LLMs during fine-tuning and propose sparse augmented tensor networks (Saten) to enhance their performance.
MAGI-1: Autoregressive Video Generation at Scale
We present MAGI-1, a world model that generates videos by autoregressively predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames.
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Large Multimodal Reasoning Models (LMRMs) have emerged as a promising paradigm, integrating modalities such as text, images, audio, and video to support complex reasoning capabilities and aiming to achieve comprehensive perception, precise understanding, and deep reasoning.
Attonsecond Streaking Phase Retrieval Via Deep Learning Methods
Attosecond streaking phase retrieval is essential for resolving electron dynamics on sub-femtosecond time scales yet traditional algorithms rely on iterative minimization and central momentum approximations that degrade accuracy for broadband pulses.
MultiMind: Enhancing Werewolf Agents with Multimodal Reasoning and Theory of Mind
Large Language Model (LLM) agents have demonstrated impressive capabilities in social deduction games (SDGs) like Werewolf, where strategic reasoning and social deception are essential.
Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data
In this paper, we introduce the image-centric multi-annotation X-ray dataset (IMAX), the first attempt to enhance the multi-task learning capabilities of medical multi-modal large language models (MLLMs) from the data construction level.
The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation
By referencing this external knowledge, RAG effectively reduces the generation of factually incorrect content and addresses hallucination issues within LLMs.
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
We introduce Seed1. 5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks.
Kimi-VL Technical Report
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2. 8B parameters in its language decoder (Kimi-VL-A3B).
DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design
Third, we propose a confidence score to evaluate the trustworthiness of the predicted results, and further develop a hybrid optimization workflow that combines operator learning with finite difference (FD) using Generalized Minimal Residual (GMRES) method for incremental solution refinement, enabling efficient and trustworthy thermal optimization.
LLM for Complex Reasoning Task: An Exploratory Study in Fermi Problems
Comparative experiments confirmed this hypothesis, demonstrating that LLMs performed better on standard FPs in terms of both accuracy and efficiency.
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation
In federated learning, fine-tuning pre-trained foundation models poses significant challenges, particularly regarding high communication cost and suboptimal model performance due to data heterogeneity between the clients.
Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing
Developing a face anti-spoofing model that meets the security requirements of clients worldwide is challenging due to the domain gap between training datasets and diverse end-user test data.
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Inference scaling empowers LLMs with unprecedented reasoning ability, with reinforcement learning as the core technique to elicit complex reasoning.
TT-GaussOcc: Test-Time Compute for Self-Supervised Occupancy Prediction via Spatio-Temporal Gaussian Splatting
Self-supervised 3D occupancy prediction offers a promising solution for understanding complex driving scenes without requiring costly 3D annotations.
Wanda++: Pruning Large Language Models via Regional Gradients
Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal performance impact.
Muon is Scalable for LLM Training
Recently, the Muon optimizer based on matrix orthogonalization has demonstrated strong results in training small-scale language models, but the scalability to larger models has not been proven.
Connector-S: A Survey of Connectors in Multi-modal Large Language Models
With the rapid advancements in multi-modal large language models (MLLMs), connectors play a pivotal role in bridging diverse modalities and enhancing model performance.
MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of Large Language Models
Large language models have demonstrated exceptional capabilities across diverse tasks, but their fine-tuning demands significant memory, posing challenges for resource-constrained environments.
CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation
Motivated by such observations, we propose CoLA and its memory-efficient implementation, CoLA-M, to replace these full-size layers with compute-efficient auto-encoders that naturally enforce low-rank activations throughout training.
CTR-Driven Advertising Image Generation with Multimodal Large Language Models
In web data, advertising images are crucial for capturing user attention and improving advertising effectiveness.
DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature Extraction for Hyperspectral Image Classification
Hyperspectral image classification presents challenges due to spectral redundancy and complex spatial-spectral dependencies.
PolaFormer: Polarity-aware Linear Attention for Vision Transformers
Linear attention has emerged as a promising alternative to softmax-based attention, leveraging kernelized feature maps to reduce complexity from quadratic to linear in sequence length.
CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs
In this paper, we introduce Contextualized Graph Retrieval-Augmented Generation (CG-RAG), a novel framework that integrates sparse and dense retrieval signals within graph structures to enhance retrieval efficiency and subsequently improve generation quality for research question answering.
Kimi k1.5: Scaling Reinforcement Learning with LLMs
Moreover, we present effective long2short methods that use long-CoT techniques to improve short-CoT models, yielding state-of-the-art short-CoT reasoning results -- e. g., 60. 8 on AIME, 94. 6 on MATH500, 47. 3 on LiveCodeBench -- outperforming existing short-CoT models such as GPT-4o and Claude Sonnet 3. 5 by a large margin (up to +550%).
Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems
Personalized learning represents a promising educational strategy within intelligent educational systems, aiming to enhance learners' practice efficiency.
DVM: Towards Controllable LLM Agents in Social Deduction Games
This control not only ensures that NPCs can adapt to varying difficulty levels during gameplay, but also provides insights into the safety and fairness of LLM agents.
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization
Compared to uncompressed training on the NVIDIA RTX 3090 GPU, our on-FPGA training achieves a memory reduction of $30\times$ to $51\times$.
A Semantic Knowledge Complementarity based Decoupling Framework for Semi-supervised Class-imbalanced Medical Image Segmentation
We also design a semantic knowledge complementarity module that adopts labeled data to guide the generation of pseudo labels and enriches the semantic features of labeled data with unlabeled data, which improves the quality of generated pseudo labels and the robustness of the overall model.
Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training
Partial differential equation (PDE) is an important math tool in science and engineering.
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models
This paper investigates representative MLLMs, focusing on their calibration across various scenarios, including before and after visual fine-tuning, as well as before and after multimodal training of the base LLMs.
PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization
As online platforms and recommendation algorithms evolve, people are increasingly trapped in echo chambers, leading to biased understandings of various issues.
Transferable Adversarial Face Attack with Text Controlled Attribute
Extensive experiments on two high-resolution face recognition datasets validate that our TCA$^2$ method can generate natural text-guided adversarial impersonation faces with high transferability.
Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness
The resilience of convolutional neural networks against input variations and adversarial attacks remains a significant challenge in image recognition tasks.
SUPERMERGE: An Approach For Gradient-Based Model Merging
A straightforward solution requires fine-tuning the model again for both existing and new tasks, which is computationally expensive and time-consuming.
Beyond Idle Channels: Unlocking Idle Space with Signal Alignment in Massive MIMO Cognitive Radio Networks
We propose a comprehensive framework that synergizes spatial spectrum sensing, signal alignment, and resource allocation, specifically designed for secondary users in CRNs.
PoTable: Towards Systematic Thinking via Stage-oriented Plan-then-Execute Reasoning on Tables
In recent years, table reasoning has garnered substantial research interest, particularly its integration with Large Language Models (LLMs) which revolutionize natural language applications.
Factorized Visual Tokenization and Generation
Visual tokenizers are fundamental to image generation.
Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware Cognitive Diagnosis Framework for Educational Contexts
To address this, we propose the Hierarchy Constraint-Aware Cognitive Diagnosis Framework (HCD), designed to more accurately represent student ability performance within real educational contexts.
DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV
Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL.
Coverage-Constrained Human-AI Cooperation with Multiple Experts
However, a notable research gap remains in effectively exploring both L2D and L2C under diverse expert knowledge to improve decision-making, particularly when constrained by the cooperation cost required to achieve a target probability for AI-only selection (i. e., coverage).
Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach
This paper presents a simple BP-free training scheme on an MCU, which makes edge training hardware design as easy as inference hardware design.
Can Language Models Learn to Skip Steps?
In this work, we study the ability to skip steps in reasoning - a hallmark of human expertise developed through practice.
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
Motivated by the success of collaborative modeling in various domains, such as recommender systems, we aim to investigate how collaborative signals among learners contribute to the diagnosis of human cognitive states (i. e., knowledge proficiency) in the context of intelligent education.
TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models
Despite advancements in TAG learning methodologies, challenges remain in explainability due to the black-box nature of existing TAG representation learning models.
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models
Nonetheless, integrating PEFT methods into FL introduces two main challenges: communication overhead and data heterogeneity.
VideoSAM: Open-World Video Segmentation
Video segmentation is essential for advancing robotics and autonomous driving, particularly in open-world settings where continuous perception and object association across video frames are critical.
ECon: On the Detection and Resolution of Evidence Conflicts
The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and challenges in detecting misinformation and managing conflicting information, or "inter-evidence conflicts."
BadCM: Invisible Backdoor Attack Against Cross-Modal Learning
In this paper, we introduce a novel bilateral backdoor to fill in the missing pieces of the puzzle in the cross-modal backdoor and propose a generalized invisible backdoor framework against cross-modal learning (BadCM).
HiReview: Hierarchical Taxonomy-Driven Automatic Literature Review Generation
In this work, we present HiReview, a novel framework for hierarchical taxonomy-driven automatic literature review generation.
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
Supervised fine-tuning (SFT) is crucial for adapting Large Language Models (LLMs) to specific tasks.
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos.
Ranked #7 on
Referring Video Object Segmentation
on Long-RVOS
VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models
Progress in Text-to-Image (T2I) models has significantly improved the generation of images from textual descriptions.
Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation
Recent advancements in generative models have significantly enhanced their capacity for image generation, enabling a wide range of applications such as image editing, completion and video editing.
Representation Learning of Geometric Trees
Geometric trees are characterized by their tree-structured layout and spatially constrained nodes and edges, which significantly impacts their topological attributes.
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to the modular nature of RAG, evaluation of long-form responses and reliability of measurements.
Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach
As online video content rapidly grows, the task of text-video retrieval (TVR) becomes increasingly important.
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning
Active Learning (AL) allows models to learn interactively from user feedback.
Towards Reliable Advertising Image Generation Using Human Feedback
In the e-commerce realm, compelling advertising images are pivotal for attracting customer attention.
Unified Lexical Representation for Interpretable Visual-Language Alignment
On the other hand, lexical representation, a vector whose element represents the similarity between the sample and a word from the vocabulary, is a natural sparse representation and interpretable, providing exact matches for individual words.
Separable Operator Networks
We provide a universal approximation theorem for SepONet proving the existence of a separable approximation to any nonlinear continuous operator.
Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning
To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles.
Learning to Complement and to Defer to Multiple Users
This process has three options: 1) AI autonomously classifies, 2) learning to complement, where AI collaborates with users, and 3) learning to defer, where AI defers to users.
A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions
Cognitive diagnosis has been developed for decades as an effective measurement tool to evaluate human cognitive status such as ability level and knowledge mastery.
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
In contrast, the human brain, with approximately 86 billion neurons, is much more energy-efficient than LLMs with similar parameters.
52B to 1T: Lessons Learned via Tele-FLM Series
Large Language Models (LLMs) represent a significant stride toward Artificial General Intelligence.
AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning
In this paper, we propose the Adaptive Zeroth-order Tensor-Train Adaption (AdaZeta) framework, specifically designed to improve the performance and convergence of the ZO methods.
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning
Inspired by the success of vanilla adversarial learning in Cross-Domain Few-Shot Learning, we take a step further and devise a model-agnostic and Primitive-Based Adversarial training (PBadv) method to deal with this problem.
Investigating Annotator Bias in Large Language Models for Hate Speech Detection
This paper serves as a crucial resource, guiding researchers and practitioners in harnessing the potential of LLMs for data annotation, thereby fostering advancements in this critical field.
TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs
To address this gap, we introduce Textual-Edge Graphs Datasets and Benchmark (TEG-DB), a comprehensive and diverse collection of benchmark textual-edge datasets featuring rich textual descriptions on nodes and edges.
OSPC: Detecting Harmful Memes with Large Language Model as a Catalyst
Utilizing the BLIP model for image captioning, PP-OCR and TrOCR for text recognition across multiple languages, and the Qwen LLM for nuanced language understanding, our system is capable of identifying harmful content in memes created in English, Chinese, Malay, and Tamil.
Adaptive Slot Attention: Object Discovery with Dynamic Slot Number
Moreover, our analysis substantiates that our method exhibits the capability to dynamically adapt the slot number according to each instance's complexity, offering the potential for further exploration in slot attention research.
Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms
In this paper, we target the realm of visual aesthetics and aim to align vision models with human aesthetic standards in a retrieval system.
Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets
By leveraging this safety direction, Legend can then leverage the semantic distances of paired responses along this direction to annotate margins automatically.
Multimodal Emotion Recognition based on Facial Expressions, Speech, and EEG
Method: In this paper, we propose a deep learning-based multimodal emotion recognition (MER) called
Benchmarking Neural Decoding Backbones towards Enhanced On-edge iBCI Applications
We executed a series of neural decoding experiments involving nonhuman primates engaged in random reaching tasks, evaluating four prospective models, Gated Recurrent Unit (GRU), Transformer, Receptance Weighted Key Value (RWKV), and Selective State Space model (Mamba), across several metrics: single-session decoding, multi-session decoding, new session fine-tuning, inference speed, calibration speed, and scalability.
Principles of Designing Robust Remote Face Anti-Spoofing Systems
Protecting digital identities of human face from various attack vectors is paramount, and face anti-spoofing plays a crucial role in this endeavor.
SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms
We plug this elastic bi-spiking mechanism in language modeling, named SpikeLM.
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models
Experimental results demonstrate that our approach surpasses the performance of both the large and small language models individually, forming a complementary advantage.
Dishonesty in Helpful and Harmless Alignment
We find that this also induces dishonesty in helpful and harmless alignment where LLMs tell lies in generating harmless responses.
Xwin-LM: Strong and Scalable Alignment Practice for LLMs
In this work, we present Xwin-LM, a comprehensive suite of alignment methodologies for large language models (LLMs).
TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations
Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum of real-world scenarios.
GRAG: Graph Retrieval-Augmented Generation
Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and, as a result, falls short in handling networked documents which are very popular in many applications such as citation graphs, social media, and knowledge graphs.
CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization
Our method also shows $\sim 2\times$ speedup than standard pre-training on a BERT-like code-generation LLM while achieving $4. 23\times$ compression ratio in pre-training.
RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models
In RefChecker, an extractor generates claim-triplets from a response, which are then evaluated by a checker against a reference.
Hallucination of Multimodal Large Language Models: A Survey
By drawing the granular classification and landscapes of hallucination causes, evaluation benchmarks, and mitigation methods, this survey aims to deepen the understanding of hallucinations in MLLMs and inspire further advancements in the field.
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing.
Tele-FLM Technical Report
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications.
DreamPBR: Text-driven Generation of High-resolution SVBRDF with Multi-modal Guidance
Prior material creation methods had limitations in producing diverse results mainly because reconstruction-based methods relied on real-world measurements and generation-based methods were trained on relatively small material datasets.
Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective
Direct Preference Optimization (DPO), which derives reward signals directly from pairwise preference data, has shown its effectiveness on aligning Large Language Models (LLMs) with human preferences.
Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs
Recent advances in large language models (LLMs) have enhanced their ability to process long input contexts.
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models
This approach reduces the computational cost to that of using just the P controller, instead of the full PID control.
EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs
In this work, we propose an initial comprehensive framework called EventGround, which aims to tackle the problem of grounding free-texts to eventuality-centric KGs for contextualized narrative reasoning.
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues.
PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model
PSALM is a powerful extension of the Large Multi-modal Model (LMM) to address the segmentation task challenges.
Ranked #2 on
Referring Expression Segmentation
on RefCoCo val
(using extra training data)
NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens
The rapid advancement of Large Language Models (LLMs) has introduced a new frontier in natural language processing, particularly in understanding and processing long-context information.
Common 7B Language Models Already Possess Strong Math Capabilities
This paper shows that the LLaMA-2 7B model with common pre-training already exhibits strong mathematical abilities, as evidenced by its impressive accuracy of 97. 7% and 72. 0% on the GSM8K and MATH benchmarks, respectively, when selecting the best response from 256 random generations.
OffensiveLang: A Community Based Implicit Offensive Language Dataset
Firstly, the existing datasets primarily rely on the collection of texts containing explicit offensive keywords, making it challenging to capture implicitly offensive contents that are devoid of these keywords.
Distilling Large Language Models for Text-Attributed Graph Learning
To address the inherent gaps between LLMs (generative models for texts) and graph models (discriminative models for graphs), we propose first to let LLMs teach an interpreter with rich textual rationale and then let a student model mimic the interpreter's reasoning without LLMs' textual rationale.
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
Various parameter-efficient fine-tuning (PEFT) techniques have been proposed to enable computationally efficient fine-tuning while maintaining model performance.
Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance
Multi-agent generative adversarial imitation learning (MAGAIL) allows multi-AUV to learn from expert demonstration instead of pre-defined reward functions, but suffers from the deficiency of requiring optimal demonstrations and not surpassing provided expert demonstrations.
See the Unseen: Better Context-Consistent Knowledge-Editing by Noises
In this paper, we empirically find that the effects of different contexts upon LLMs in recalling the same knowledge follow a Gaussian-like distribution.
Real-Time FJ/MAC PDE Solvers via Tensorized, Back-Propagation-Free Optical PINN Training
Despite the ultra-high speed of optical neural networks, training a PINN on an optical chip is hard due to (1) the large size of photonic devices, and (2) the lack of scalable optical memory devices to store the intermediate results of back-propagation (BP).
Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style
Furthermore, for products with specific and fine-grained requirements in layout, elements, etc, a Personality-Wise Generator is devised to learn such personalized style directly from a reference image to resolve textual ambiguities, and is trained in a self-supervised manner for more efficient training data usage.
Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives
Consequently, we refine the cognitive states of cold-start students as diagnostic outcomes via virtual data, aligning with the diagnosis-oriented goal.
Non-Euclidean Spatial Graph Neural Network
Besides, existing spatial network representation learning methods can only consider networks embedded in Euclidean space, and can not well exploit the rich geometric information carried by irregular and non-uniform non-Euclidean space.
Planning and Rendering: Towards Product Poster Generation with Diffusion Models
At the planning stage, we propose a PlanNet to generate the layout of the product and other visual components considering both the appearance features of the product and semantic features of the text, which improves the diversity and rationality of the layouts.
BiPFT: Binary Pre-trained Foundation Transformer with Low-rank Estimation of Binarization Residual Polynomials
Specifically, we first analyze the binarization error in self-attention operations and derive the polynomials of binarization error.
Segment and Caption Anything
We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions.
RTQ: Rethinking Video-language Understanding Based on Image-text Model
Recent advancements in video-language understanding have been established on the foundation of image-text models, resulting in promising outcomes due to the shared knowledge between images and videos.
Ranked #9 on
Video Captioning
on MSR-VTT
(using extra training data)
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields.
Learning to Complement with Multiple Humans
The ill-posedness of the LNL task requires the adoption of strong assumptions or the use of multiple noisy labels per training image, resulting in accurate models that work well in isolation but fail to optimise human-AI collaborative classification (HAI-CC).
A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources, and the adaptive training of a chatbot with domain-specific inquiries.
Asymptotically Fair Participation in Machine Learning Models: an Optimal Control Perspective
Moreover, we design a surrogate retention system based on existing literature on evolutionary population dynamics to approximate the dynamics of distribution shifts on active user counts, from which the objective of achieving asymptotically fair participation is formulated as an optimal control problem, and the control variables are considered as the model parameters.
WinNet: Make Only One Convolutional Layer Effective for Time Series Forecasting
Deep learning models have recently achieved significant performance improvements in time series forecasting.
Ranked #15 on
Time Series Forecasting
on ETTh1 (336) Multivariate
FP8-LM: Training FP8 Large Language Models
In this paper, we explore FP8 low-bit data formats for efficient training of large language models (LLMs).
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts
As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a great complementarity to each other on math reasoning tasks.
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes.
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding
Analogy-making between narratives is crucial for human reasoning.
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language Models (LLMs).
Transferable Deep Clustering Model
Deep learning has shown remarkable success in the field of clustering recently.
Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data
Large language models (LLMs) have achieved impressive performance on many natural language processing tasks.
Large Language Models for Spatial Trajectory Patterns Mining
Identifying anomalous human spatial trajectory patterns can indicate dynamic changes in mobility behavior with applications in domains like infectious disease monitoring and elderly care.
Balancing Specialized and General Skills in LLMs: The Impact of Modern Tuning and Data Strategy
This paper introduces a multifaceted methodology for fine-tuning and evaluating large language models (LLMs) for specialized monetization tasks.
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training
Our extensive experiments show that DeepZero achieves state-of-the-art (SOTA) accuracy on ResNet-20 trained on CIFAR-10, approaching FO training performance for the first time.
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Furthermore, we propose a multi-view fusion layer based temporal module which is equipped with a set of object slots and interacts with features from different views by attention mechanism to fulfill sufficient object representation completion.
Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation
In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions.
Unsupervised Open-Vocabulary Object Localization in Videos
In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization.
FLM-101B: An Open LLM and How to Train It with $100K Budget
Large language models (LLMs) are considered important approaches towards foundational machine intelligence, achieving remarkable success in Natural Language Processing and multimodal tasks, among others.
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions.
Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines.
Coarse-to-Fine Amodal Segmentation with Shape Prior
To address this issue, we propose a convolution refine module to inject fine-grained information and provide a more precise amodal object segmentation based on visual features and coarse-predicted segmentation.
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users seeking access to data across various modalities.
Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks
Backward propagation (BP) is widely used to compute the gradients in neural network training.
DETR Doesn't Need Multi-Scale or Locality Design
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder.
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations.
Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning
To this end, we propose partial vessels annotation (PVA) based on the challenges of coronary artery segmentation and clinical diagnostic characteristics.
A review of dynamics design methods for high-speed and high-precision CNC machine tool feed systems
With the development of CNC machine tools toward high speed and high precision, the traditional static design methods can hardly meet the demand.
Mutual Query Network for Multi-Modal Product Image Segmentation
Product image segmentation is vital in e-commerce.
Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations
Discourse analysis is an important task because it models intrinsic semantic structures between sentences in a document.
Relation-Aware Diffusion Model for Controllable Poster Layout Generation
To advance research in this field, we have constructed a poster layout dataset named CGL-Dataset V2.
A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels
In this paper, we propose a novel gradient-based approach to enable the detection of noisy labels for the online learning of model parameters, named Online Gradient-based Robust Selection (OGRS).
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning
It has always been an important yet challenging problem to control language models to avoid generating texts with undesirable attributes, such as toxic language and unnatural repetition.
Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding
To improve the convergence, a layer-by-layer distillation is applied to distill a quantized and tensor-compressed student model from a pre-trained transformer.
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
Motivated by the fact that all event structures can be inferred from AMR, this work reformulates EAE as a link prediction problem on AMR graphs.
Abstract Meaning Representation
Event Argument Extraction
+2
Exploiting Abstract Meaning Representation for Open-Domain Question Answering
The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database.
Evaluating Open-QA Evaluation
This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs).
Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations
Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine incorrect queries.
Masked Structural Growth for 2x Faster Language Model Pre-training
In terms of growth schedule, the impact of each single dimension on a schedule's efficiency is under-explored by existing work.
A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework with Gray Code Representation
Additionally, the Gray code representation and the differential prediction paradigm are designed to cope with the high-bit misclassifications of the BE representation, which significantly reduces the outliers in the predictions.
Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control
The booming air transportation industry inevitably burdens air traffic controllers' workload, causing unexpected human factor-related incidents.
STM-UNet: An Efficient U-shaped Architecture Based on Swin Transformer and Multi-scale MLP for Medical Image Segmentation
Deep learning based models for medical image segmentation have made great progress in recent years.
VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping
In argumentative writing, writers must brainstorm hierarchical writing goals, ensure the persuasiveness of their arguments, and revise and organize their plans through drafting.
Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios
Multi-view clustering (MVC) aims at exploring category structures among multi-view data in self-supervised manners.
DeepMIM: Deep Supervision for Masked Image Modeling
Deep supervision, which involves extra supervisions to the intermediate features of a neural network, was widely used in image classification in the early deep learning era since it significantly reduces the training difficulty and eases the optimization like avoiding gradient vanish over the vanilla training.
Particle-based Online Bayesian Sampling
Online optimization has gained increasing interest due to its capability of tracking real-world streaming data.
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design
In this paper, for the first time, we propose DeepOHeat, a physics-aware operator learning framework to predict the temperature field of a family of heat equations with multiple parametric or non-parametric design configurations.
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
PIFON-EPT is the first method that can simultaneously reconstruct EP and transmit fields from incomplete noisy MR measurements, providing new opportunities for EPT research.
Side Adapter Network for Open-Vocabulary Semantic Segmentation
A side network is attached to a frozen CLIP model with two branches: one for predicting mask proposals, and the other for predicting attention bias which is applied in the CLIP model to recognize the class of masks.
Ranked #5 on
Zero Shot Segmentation
on Segmentation in the Wild
Tensorized Optical Multimodal Fusion Network
We propose the first tensorized optical multimodal fusion network architecture with a self-attention mechanism and low-rank tensor fusion.
Apples and Oranges? Assessing Image Quality over Content Recognition
A sequential spatial-channel attention module is proposed to simulate the visual attention and contrast sensitivity mechanisms that are crucial for content recognition and quality assessment.
FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU.
All in Tokens: Unifying Output Space of Visual Tasks via Soft Token
With these new techniques and other designs, we show that the proposed general-purpose task-solver can perform both instance segmentation and depth estimation well.
Ranked #27 on
Monocular Depth Estimation
on NYU-Depth V2
TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models
Our TinyMIM model of tiny size achieves 79. 6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget.
iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-Training for Visual Recognition
This paper presents a method that effectively combines two prevalent visual recognition methods, i. e., image classification and contrastive language-image pre-training, dubbed iCLIP.
DETR Does Not Need Multi-Scale or Locality Design
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder.
Ranked #12 on
Object Detection
on COCO test-dev
Dual Learning with Dynamic Knowledge Distillation for Partially Relevant Video Retrieval
During the knowledge distillation, an inheritance student branch is devised to absorb the knowledge from the teacher model.
Improving CLIP Fine-tuning Performance
Experiments suggest that the feature map distillation approach significantly boosts the fine-tuning performance of CLIP models on several typical downstream vision tasks.
Refined Edge Usage of Graph Neural Networks for Edge Prediction
However, existing methods lack elaborate design regarding the distinctions between two tasks that have been frequently overlooked: (i) edges only constitute the topology in the node classification task but can be used as both the topology and the supervisions (i. e., labels) in the edge prediction task; (ii) the node classification makes prediction over each individual node, while the edge prediction is determinated by each pair of nodes.
EASpace: Enhanced Action Space for Policy Transfer
In this paper, a novel algorithm named EASpace (Enhanced Action Space) is proposed, which formulates macro actions in an alternative form to accelerate the learning process using multiple available sub-optimal expert policies.
CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection
Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion.
KPT: Keyword-guided Pre-training for Grounded Dialog Generation
For the former, the grounding knowledge consists of keywords extracted from the response.
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants.
Exploring Discrete Diffusion Models for Image Captioning
The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one.
Could Giant Pretrained Image Models Extract Universal Representations?
In this paper, we present a study of frozen pretrained models when applied to diverse and representative computer vision tasks, including object detection, semantic segmentation and video action recognition.
Ranked #3 on
Action Recognition In Videos
on Kinetics-400
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees
RLET iteratively performs single step reasoning with sentence selection and deduction generation modules, from which the training signal is accumulated across the tree with elaborately designed aligned reward function that is consistent with the evaluation.
DORE: Document Ordered Relation Extraction based on Generative Framework
In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models.
Conversation Disentanglement with Bi-Level Contrastive Learning
Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations.
MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks
Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue.
Self-supervised Amodal Video Object Segmentation
The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens.
Bridging the Gap to Real-World Object-Centric Learning
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world.
Vega-MT: The JD Explore Academy Translation System for WMT22
As for model sizes, we scale the Transformer-Big up to the extremely large model that owns nearly 4. 7 Billion parameters, to fully enhance the model capacity for our Vega-MT.
Ranked #1 on
Machine Translation
on WMT 2022 English-Russian
Whole-Body Lesion Segmentation in 18F-FDG PET/CT
There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers.
