5 papers · 1 filter
Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models
Xiaojie Gu, Sherry T. Tong, Aosong Feng +8
Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, especially in multi-hop QA benchmarks witho…
Med-CoReasoner: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning
Fan Gao, Sherry T. Tong, Jiwoong Sohn +11
While reasoning-enhanced large language models perform strongly on English medical tasks, a persistent multilingual gap remains, with substantially weaker reasoning in local langua…
From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs
Yingjian Chen, Haoran Liu, Yinhong Liu +7
Large Language Models (LLMs) show strong reasoning ability in open-domain question answering, yet their reasoning processes are typically linear and often logically inconsistent. I…
Investigating the Multilingual Calibration Effects of Language Model Instruction-Tuning
Jerry Huang, Peng Lu, Qiuhao Zeng +5
Ensuring that deep learning models are well-calibrated in terms of their predictive uncertainty is essential in maintaining their trustworthiness and reliability, yet despite incre…
Better Explain Transformers by Illuminating Important Information
Linxin Song, Yan Cui, Ao Luo +2
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings. Prior methods explain Transformers…