18 citations · 25 across the 9 of their papers we have counts for
9 papers
CLUE: Non-parametric Verification from Experience via Hidden-State Clustering
Zhenwen Liang, Ruosen Li, Yujun Zhou +5
Assessing the quality of Large Language Model (LLM) outputs presents a critical challenge. Previous methods either rely on text-level information (e.g., reward models, majority vot…
R-Zero: Self-Evolving Reasoning LLM from Zero Data
Chengsong Huang, Wenhao Yu, Xiaoyang Wang +6
Self-evolving Large Language Models (LLMs) offer a scalable path toward super-intelligence by autonomously generating, refining, and learning from their own experiences. However, e…
AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control
Ruosen Li, Ziming Luo, Quan Zhang +4
Large reasoning models (LRMs) achieve impressive reasoning capabilities by generating lengthy chain-of-thoughts, but this "overthinking" incurs high latency and cost without commen…
Multimodal Reference Visual Grounding
Yangxiao Lu, Ruosen Li, Liqiang Jing +5
Visual grounding focuses on detecting objects from images based on language expressions. Recent Large Vision-Language Models (LVLMs) have significantly advanced visual grounding pe…
FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning
Ruosen Li, Ziming Luo, Xinya Du
Hallucinations in large language models (LLMs) pose significant challenges in tasks requiring complex multi-step reasoning, such as mathematical problem-solving. Existing approache…
IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering
Ruosen Li, Ruochen Li, Barry Wang +1
To evaluate Large Language Models (LLMs) for question answering (QA), traditional methods typically focus on assessing single-turn responses to given questions. However, this appro…
Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning
Ruosen Li, Xinya Du
Neural models, including large language models (LLMs), achieve superior performance on multi-hop question-answering. To elicit reasoning capabilities from LLMs, recent works propos…
FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models
Liqiang Jing, Ruosen Li, Yunmo Chen +1
We introduce FaithScore (Faithfulness to Atomic Image Facts Score), a reference-free and fine-grained evaluation metric that measures the faithfulness of the generated free-form an…
PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations
Ruosen Li, Teerth Patel, Xinya Du
Nowadays, the quality of responses generated by different modern large language models (LLMs) is hard to evaluate and compare automatically. Recent studies suggest and predominantl…