Search Results for author:
Found 13 papers, 6 papers with code
A Comprehensive Survey on Long Context Language Modeling
With the growing number of long documents, dialogues, and other textual data, it is important to develop Long Context Language Models (LCLMs) that can process and analyze extensive inputs in an effective and efficient way.
Bench2FreeAD: A Benchmark for Vision-based End-to-end Navigation in Unstructured Robotic Environments
Thus, this paper presents the first dataset targeting E2E robot navigation tasks in unstructured scenarios, and provides a benchmark based on vision-based E2E autonomous driving algorithms to facilitate the development of E2E navigation technology for logistics and service robots.
Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?
In this paper, to understand the qualities of these long CoTs and measure the critique abilities of existing LLMs on these long CoTs, we introduce the DeltaBench, including the generated long CoTs from different o1-like models (e. g., QwQ, DeepSeek-R1) for different reasoning tasks (e. g., Math, Code, General Reasoning), to measure the ability to detect errors in long CoT reasoning.
AIR: Complex Instruction Generation via Automatic Iterative Refinement
With the development of large language models, their ability to follow simple instructions has significantly improved.
"See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models
The evaluation of factual accuracy in large vision language models (LVLMs) has lagged behind their rapid development, making it challenging to fully reflect these models' knowledge capacity and reliability.
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training
Complex instruction-following with elaborate constraints is imperative for Large Language Models (LLMs).
Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation
Direct Preference Optimization (DPO) has been demonstrated to be highly effective in mitigating hallucinations in Large Vision Language Models (LVLMs) by aligning their outputs more closely with human preferences.
Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models
Based on Chinese SimpleQA, we perform a comprehensive evaluation on the factuality abilities of existing LLMs.
2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision
Recent advancements in Direct Preference Optimization (DPO) have significantly enhanced the alignment of Large Language Models (LLMs) with human preferences, owing to its simplicity and effectiveness.
Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention
Previous approaches capture language bias from a coarse-grained perspective.
GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models
Long-context capabilities are essential for large language models (LLMs) to tackle complex and long-input tasks.
Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction
However, fine-grained matching often requires laborious manual annotation, and rich interactions between instances and label descriptions come with significant computational overhead.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
