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Found 35 papers, 17 papers with code
Adding Additional Control to One-Step Diffusion with Joint Distribution Matching
This asymmetric distillation scheme enables our one-step student to handle controls unknown to the teacher model and facilitates improved classifier-free guidance (CFG) usage and seamless integration of human feedback learning (HFL).
MPO: Boosting LLM Agents with Meta Plan Optimization
Recent advancements in large language models (LLMs) have enabled LLM-based agents to successfully tackle interactive planning tasks.
Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
Despite extensive research efforts focused on OOD detection on images, OOD detection on nodes in graph learning remains underexplored.
More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression
As large language models (LLMs) process increasing context windows, the memory usage of KV cache has become a critical bottleneck during inference.
VLRewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models
Vision-language generative reward models (VL-GenRMs) play a crucial role in aligning and evaluating multimodal AI systems, yet their own evaluation remains under-explored.
MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures
Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their development.
Harnessing Webpage UIs for Text-Rich Visual Understanding
Text-rich visual understanding-the ability to process environments where dense textual content is integrated with visuals-is crucial for multimodal large language models (MLLMs) to interact effectively with structured environments.
AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories
Furthermore, we fine-tune LLMs on AgentBank to get a series of agent models, Samoyed.
Urban Region Pre-training and Prompting: A Graph-based Approach
Urban region representation is crucial for various urban downstream tasks.
The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism
Current evaluations of large language models (LLMs) often overlook non-determinism, typically focusing on a single output per example.
Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement
Large language model agents have exhibited exceptional performance across a range of complex interactive tasks.
VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
Multimodal Large Language models (MLLMs) have shown promise in web-related tasks, but evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks.
CoUDA: Coherence Evaluation via Unified Data Augmentation
Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models.
Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
This iterative cycle of exploration and training fosters continued improvement in the agents.
MMBaT: A Multi-task Framework for mmWave-based Human Body Reconstruction and Translation Prediction
Human body reconstruction with Millimeter Wave (mmWave) radar point clouds has gained significant interest due to its ability to work in adverse environments and its capacity to mitigate privacy concerns associated with traditional camera-based solutions.
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.
Rationale-Enhanced Language Models are Better Continual Relation Learners
Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations.
Categorizing Flight Paths using Data Visualization and Clustering Methodologies
This work leverages the U. S. Federal Aviation Administration's Traffic Flow Management System dataset and DV8, a recently developed tool for highly interactive visualization of air traffic data, to develop clustering algorithms for categorizing air traffic by their varying flight paths.
PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training
To decouple train length from target length for efficient context window extension, we propose Positional Skip-wisE (PoSE) training that smartly simulates long inputs using a fixed context window.
Advanced Underwater Image Restoration in Complex Illumination Conditions
To validate the effectiveness of our approach, we conducted extensive experiments on simulated and real-world datasets.
Semihierarchical Reconstruction and Weak-area Revisiting for Robotic Visual Seafloor Mapping
Despite impressive results achieved by many on-land visual mapping algorithms in the recent decades, transferring these methods from land to the deep sea remains a challenge due to harsh environmental conditions.
RestGPT: Connecting Large Language Models with Real-World RESTful APIs
To address the practical challenges of tackling complex instructions, we propose RestGPT, which exploits the power of LLMs and conducts a coarse-to-fine online planning mechanism to enhance the abilities of task decomposition and API selection.
RepCL: Exploring Effective Representation for Continual Text Classification
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.
DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction.
Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?
We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate Python programming courses at the postsecondary level.
Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process.
Calibrating Factual Knowledge in Pretrained Language Models
However, we find that facts stored in the PLMs are not always correct.
ConFiguRe: Exploring Discourse-level Chinese Figures of Speech
Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type.
Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach
2) Balanced Tuning (BT) finetunes the model on the balanced memory data.
Skill requirements in job advertisements: A comparison of skill-categorization methods based on explanatory power in wage regressions
In this paper, we compare different methods to extract skill requirements from job advertisements.
Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances
For each instance in a batch, we involve other instances in the same batch to interact with it.
Marine Bubble Flow Quantification Using Wide-Baseline Stereo Photogrammetry
Beside reporting the steps to make bubble characterization robust and autonomous, we carefully evaluate the reachable accuracy to be in the range of 1-2\% of the bubble radius and propose a novel auto-calibration procedure that, due to the lack of point correspondences, uses only the silhouettes of bubbles.
Robustly Removing Deep Sea Lighting Effects for Visual Mapping of Abyssal Plains
The majority of Earth's surface lies deep in the oceans, where no surface light reaches.
Refractive Geometry for Underwater Domes
Underwater cameras are typically placed behind glass windows to protect them from the water.
