8 citations · 17 across the 27 of their papers we have counts for
7 papers · 1 filter
MASH: Modeling Abstention via Selective Help-Seeking
Mustafa Omer Gul, Claire Cardie, Tanya Goyal
LLMs cannot reliably recognize their parametric knowledge boundaries and often hallucinate answers to outside-of-boundary questions. In this paper, we introduce MASH (Modeling Abst…
Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive Rewards
Jinyan Su, Claire Cardie
Large language models (LLMs) have demonstrated strong reasoning abilities in mathematical tasks, often enhanced through reinforcement learning (RL). However, RL-trained models freq…
HAPO: Training Language Models to Reason Concisely via History-Aware Policy Optimization
Chengyu Huang, Zhengxin Zhang, Claire Cardie
While scaling the length of responses at test-time has been shown to markedly improve the reasoning abilities and performance of large language models (LLMs), it often results in v…
Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs
Jinyan Su, Jennifer Healey, Preslav Nakov +1
Large language models (LLMs) are increasingly optimized for long reasoning, under the assumption that more reasoning leads to better performance. However, emerging evidence suggest…
Reasoning Court: Combining Reasoning, Action, and Judgment for Multi-Hop Reasoning
Jingtian Wu, Claire Cardie
While large language models (LLMs) have demonstrated strong capabilities in tasks like question answering and fact verification, they continue to suffer from hallucinations and rea…
Are Triggers Needed for Document-Level Event Extraction?
Shaden Shaar, Wayne Chen, Maitreyi Chatterjee +3
Most existing work on event extraction has focused on sentence-level texts and presumes the identification of a trigger-span -- a word or phrase in the input that evokes the occurr…