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Found 77 papers, 39 papers with code
The Hallucination Dilemma: Factuality-Aware Reinforcement Learning for Large Reasoning Models
Large language models (LLMs) have significantly advanced in reasoning tasks through reinforcement learning (RL) optimization, achieving impressive capabilities across various challenging benchmarks.
ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework
Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary systems with opaque architectures.
TokBench: Evaluating Your Visual Tokenizer before Visual Generation
In this work, we reveal the limitations of visual tokenizers and VAEs in preserving fine-grained features, and propose a benchmark to evaluate reconstruction performance for two challenging visual contents: text and face.
Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning
Inspired by the dual process theory in cognitive science, we propose Adaptive Cognition Policy Optimization (ACPO), a reinforcement learning framework that enables LRMs to achieve efficient reasoning through adaptive cognitive allocation and dynamic system switch.
Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User
With this simulated user, automatic multi-turn interaction with CRSs can be effectively conducted.
MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation
While single-species protein function prediction methods have achieved remarkable success, multi-species protein function prediction methods are still in the stage of using PPI networks and sequence features.
Can Large Language Models Help Experimental Design for Causal Discovery?
Designing proper experiments and selecting optimal intervention targets is a longstanding problem in scientific or causal discovery.
LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation
To address these challenges, we propose Long Context Pre-training with Restoration Distillation (LongReD), a novel approach designed to mitigate short-text performance degradation through minimizing the distribution discrepancy between the extended and original models.
Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking
We reformulate the task as a progressive information collection process with a knowledge memory and unite an adaptive checklist with multi-perspective reward modeling in MCTS.
Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking
Large language models (LLMs) demonstrate exceptional capabilities, yet still face the hallucination issue.
Think&Cite: Improving Attributed Text Generation with Self-Guided Tree Search and Progress Reward Modeling
Despite their outstanding capabilities, large language models (LLMs) are prone to hallucination and producing factually incorrect information.
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks.
Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling
Bilevel Optimization has experienced significant advancements recently with the introduction of new efficient algorithms.
PartGLEE: A Foundation Model for Recognizing and Parsing Any Objects
We present PartGLEE, a part-level foundation model for locating and identifying both objects and parts in images.
Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment
However, this method may result in inefficient knowledge memorization due to a lack of awareness of knowledge utilization and imposes substantial demands on LLMs to simultaneously learn knowledge utilization and format alignment with limited training samples.
MKDTI: Predicting drug-target interactions via multiple kernel fusion on graph attention network
In our work, we formulate a model called MKDTI by extracting kernel information from various layer embeddings of a graph attention network.
LLMBox: A Comprehensive Library for Large Language Models
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs.
YuLan: An Open-source Large Language Model
This paper presents the development of YuLan, a series of open-source LLMs with $12$ billion parameters.
Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector
Hallucination detection is a challenging task for large language models (LLMs), and existing studies heavily rely on powerful closed-source LLMs such as GPT-4.
Exploring Context Window of Large Language Models via Decomposed Positional Vectors
Based on our findings, we design two training-free context window extension methods, positional vector replacement and attention window extension.
Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising
To this end, we propose to analyze and redesign the channel and spatial attentions to meet the blind-spot requirement.
ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting
In response to this challenge, we present an empirical investigation of CoT prompting and introduce CoTGenius, a novel framework designed for the automatic generation of superior CoT prompts.
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
By combining the improvements in both architecture and training, our proposed REAR can better utilize external knowledge by effectively perceiving the relevance of retrieved documents.
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models
To tackle the LLM hallucination, three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them (mitigation).
Device-Wise Federated Network Pruning
Neural network pruning particularly channel pruning is a widely used technique for compressing deep learning models to enable their deployment on edge devices with limited resources.
On the steerability of large language models toward data-driven personas
Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented.
BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models
Recently, multiple studies have committed to extending the context length and enhancing the long text modeling capabilities of LLMs.
Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition
In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i. e., UNIC).
Zero-shot Visual Question Answering with Language Model Feedback
In this paper, we propose a novel language model guided captioning approach, LAMOC, for knowledge-based visual question answering (VQA).
Learning to Imagine: Visually-Augmented Natural Language Generation
People often imagine relevant scenes to aid in the writing process.
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i. e., content that conflicts with the source or cannot be verified by the factual knowledge.
The Web Can Be Your Oyster for Improving Large Language Models
In order to further improve the capacity of LLMs for knowledge-intensive tasks, we consider augmenting LLMs with the large-scale web using search engine.
GlyphDiffusion: Text Generation as Image Generation
In this way, conditional text generation can be cast as a glyph image generation task, and it is then natural to apply continuous diffusion models to discrete texts.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas.
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems
In this work, we investigate Federated Bilevel Optimization problems and propose a communication-efficient algorithm, named FedBiOAcc.
FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging
This matches the best known rate for first-order FL algorithms and \textbf{FedDA-MVR} is the first adaptive FL algorithm that achieves this rate.
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2. 0, focusing on the use of pre-trained language models (PLMs).
Ranked #1 on
Abstractive Text Summarization
on CNN/Daily Mail
Adaptive Federated Minimax Optimization with Lower Complexities
To fill this gap, in the paper, we study a class of nonconvex minimax optimization, and propose an efficient adaptive federated minimax optimization algorithm (i. e., AdaFGDA) to solve these distributed minimax problems.
FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings
Therefore, Federated Recommender (FedRec) systems are proposed to mitigate privacy concerns to non-distributed recommender systems.
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
However, NAR models usually generate texts of lower quality due to the absence of token dependency in the output text.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
MVP: Multi-task Supervised Pre-training for Natural Language Generation
Motivated by the success of supervised pre-training, we propose Multi-task superVised Pre-training (MVP) for natural language generation.
Communication-Efficient Robust Federated Learning with Noisy Labels
Bilevel optimization problem is a type of optimization problem with two levels of entangled problems.
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models
In this paper, we present a large-scale empirical study on general language ability evaluation of PLMs (ElitePLM).
Learning to Transfer Prompts for Text Generation
First, PTG learns a set of source prompts for various source generation tasks and then transfers these prompts as target prompts to perform target generation tasks.
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction
Specifically, we first propose the FedBiO, a deterministic gradient-based algorithm and we show it requires $O(\epsilon^{-2})$ number of iterations to reach an $\epsilon$-stationary point.
Correction of out-of-focus microscopic images by deep learning
Results To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones
However, BiRNN is intrinsically offline because it uses backward recurrent modules to propagate from the last to current frames, which causes high latency and large memory consumption.
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models
The results show that WuDaoMM can be applied as an efficient dataset for VLPMs, especially for the model in text-to-image generation task.
A Survey of Vision-Language Pre-Trained Models
In this paper, we review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs).
Context-Tuning: Learning Contextualized Prompts for Natural Language Generation
Secondly, we use continuous inverse prompting to improve the process of natural language generation by modeling an inverse generation process from output to input, making the generated text more relevant to the inputs.
Pretrained Language Models for Text Generation: A Survey
We begin with introducing three key aspects of applying PLMs to text generation: 1) how to encode the input into representations preserving input semantics which can be fused into PLMs; 2) how to design an effective PLM to serve as the generation model; and 3) how to effectively optimize PLMs given the reference text and to ensure that the generated texts satisfy special text properties.
A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse
Combining our new formulation with the alternative update of the inner and outer variables, we propose an efficient fully single loop algorithm.
Enhanced Bilevel Optimization via Bregman Distance
Specifically, we propose a bilevel optimization method based on Bregman distance (BiO-BreD) to solve deterministic bilevel problems, which achieves a lower computational complexity than the best known results.
Compositional federated learning: Applications in distributionally robust averaging and meta learning
In the paper, we propose an effective and efficient Compositional Federated Learning (ComFedL) algorithm for solving a new compositional Federated Learning (FL) framework, which frequently appears in many data mining and machine learning problems with a hierarchical structure such as distributionally robust FL and model-agnostic meta learning (MAML).
BiAdam: Fast Adaptive Bilevel Optimization Methods
To fill this gap, in the paper, we propose a novel fast adaptive bilevel framework to solve stochastic bilevel optimization problems that the outer problem is possibly nonconvex and the inner problem is strongly convex.
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients
To fill this gap, we propose a faster and universal framework of adaptive gradients (i. e., SUPER-ADAM) by introducing a universal adaptive matrix that includes most existing adaptive gradient forms.
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models
This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG).
Pretrained Language Models for Text Generation: A Survey
In this paper, we present an overview of the major advances achieved in the topic of PLMs for text generation.
Knowledge-based Review Generation by Coherence Enhanced Text Planning
For global coherence, we design a hierarchical self-attentive architecture with both subgraph- and node-level attention to enhance the correlations between subgraphs.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation
In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework.
Lee at SemEval-2020 Task 5: ALBERT Model Based on the Maximum Ensemble Strategy and Different Data Sampling Methods for Detecting Counterfactual Statements
Our methods achieved a F1 score of 0. 85 in subtask A.
Lijunyi at SemEval-2020 Task 4: An ALBERT Model Based Maximum Ensemble with Different Training Sizes and Depths for Commonsense Validation and Explanation
This article describes the system submitted to SemEval 2020 Task 4: Commonsense Validation and Explanation.
Lee at SemEval-2020 Task 12: A BERT Model Based on the Maximum Self-ensemble Strategy for Identifying Offensive Language
We only participate in the English part of subtask A, which aims to identify offensive languages in English.
Leveraging Class Hierarchy for Code Comprehension
Object-oriented programming languages enable a hierarchical class structure, which provides rich contextual information to guide code comprehension and synthesis.
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence.
Improved Bilevel Model: Fast and Optimal Algorithm with Theoretical Guarantee
In this paper, we propose an improved bilevel model which converges faster and better compared to the current formulation.
Faster Secure Data Mining via Distributed Homomorphic Encryption
Due to the rising privacy demand in data mining, Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field.
Generating Realistic Stock Market Order Streams
We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs).
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator.
YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction
We participated in the BioNLP 2019 Open Shared Tasks: binary relation extraction of SeeDev task.
Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding
In this paper, we propose a novel review generation model by characterizing an elaborately designed aspect-aware coarse-to-fine generation process.
Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums
In this paper, we describe a suggestion mining system that participated in SemEval 2019 Task 9, SubTask A - Suggestion Mining from Online Reviews and Forums.
