1 citations · 2 across the 5 of their papers we have counts for
12 papers
RefineBench: Evaluating Refinement Capability of Language Models via Checklists
Young-Jun Lee, Seungone Kim, Byung-Kwan Lee +6
Can language models (LMs) self-refine their own responses? This question is increasingly relevant as a wide range of real-world user interactions involve refinement requests. Howev…
VideoJudge: Bootstrapping Enables Scalable Supervision of MLLM-as-a-Judge for Video Understanding
Abdul Waheed, Zhen Wu, Dareen Alharthi +2
Precisely evaluating video understanding models remains challenging: commonly used metrics such as BLEU, ROUGE, and BERTScore fail to capture the fineness of human judgment, while…
OptimalThinkingBench: Evaluating Over and Underthinking in LLMs
Pranjal Aggarwal, Seungone Kim, Jack Lanchantin +4
Thinking LLMs solve complex tasks at the expense of increased compute and overthinking on simpler problems, while non-thinking LLMs are faster and cheaper but underthink on harder…
Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning
Maggie Huan, Yuetai Li, Tuney Zheng +6
Math reasoning has become the poster child of progress in large language models (LLMs), with new models rapidly surpassing human-level performance on benchmarks like MATH and AIME.…
FREESON: Retriever-Free Retrieval-Augmented Reasoning via Corpus-Traversing MCTS
Chaeeun Kim, Seungone Kim
Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in multi-step reasoning and calling search engines at appropriate steps. However, existing retrieval-augment…
The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think
Seongyun Lee, Seungone Kim, Minju Seo +9
Long chain-of-thought (CoT) is an essential ingredient in effective usage of modern large language models, but our understanding of the reasoning strategies underlying these capabi…