4 citations · 9 across the 9 of their papers we have counts for
10 papers · 1 filter
ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning
Binglin Zhou, Peng Shi, Ryo Kamoi +2
Multimodal Scientific Claim Verification (MSCV) requires models to verify scientific claims using visually grounded evidence from papers, including figures, tables, charts, and tex…
TIAR: Trajectory-Informed Advantage Reweighting for LLM Abstention Learning
Muyu Pan, Shu Zhao, Nan Zhang +4
This paper investigates large language model (LLM) abstention learning, specifically using ternary reward, which incentivize truthfulness in large language models. This paper exten…
Efficient PRM Training Data Synthesis via Formal Verification
Ryo Kamoi, Yusen Zhang, Nan Zhang +4
Process Reward Models (PRMs) have emerged as a promising approach for improving LLM reasoning capabilities by providing process supervision over reasoning traces. However, existing…
HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?
Yusen Zhang, Wenliang Zheng, Aashrith Madasu +14
High-resolution image (HRI) understanding aims to process images with a large number of pixels, such as pathological images and agricultural aerial images, both of which can exceed…
SiReRAG: Indexing Similar and Related Information for Multihop Reasoning
Nan Zhang, Prafulla Kumar Choubey, Alexander Fabbri +5
Indexing is an important step towards strong performance in retrieval-augmented generation (RAG) systems. However, existing methods organize data based on either semantic similarit…
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
Jiangshu Du, Yibo Wang, Wenting Zhao +37
This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and ques…
When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
Ryo Kamoi, Yusen Zhang, Nan Zhang +2
Self-correction is an approach to improving responses from large language models (LLMs) by refining the responses using LLMs during inference. Prior work has proposed various self-…
Pruning as a Domain-specific LLM Extractor
Nan Zhang, Yanchi Liu, Xujiang Zhao +5
Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. However, the escalation in model size also engenders substantial deployment cos…
Evaluating LLMs at Detecting Errors in LLM Responses
Ryo Kamoi, Sarkar Snigdha Sarathi Das, Renze Lou +12
With Large Language Models (LLMs) being widely used across various tasks, detecting errors in their responses is increasingly crucial. However, little research has been conducted o…
Fair Abstractive Summarization of Diverse Perspectives
Yusen Zhang, Nan Zhang, Yixin Liu +9
People from different social and demographic groups express diverse perspectives and conflicting opinions on a broad set of topics such as product reviews, healthcare, law, and pol…