1 citations · 1 across the 5 of their papers we have counts for
4 papers · 1 filter
On the Token Value Inequality in Efficient Reasoning
Runjia Zeng, Hang Hua, Yiyang Liu +5
Chain-of-Thought reasoning has enabled large language models to achieve substantial performance gains on complex tasks. However, these gains come at the cost of dramatically increa…
Where Fake Citations Are Made: Tracing Field-Level Hallucination to Specific Neurons in LLMs
Yuefei Chen, Yihao Quan, Xiaodong Lin +1
LLMs frequently generate fictitious yet convincing citations, often expressing high confidence even when the underlying reference is wrong. We study this failure across 9 models an…
TokenSeek: Memory Efficient Fine Tuning via Instance-Aware Token Ditching
Runjia Zeng, Qifan Wang, Qiang Guan +6
Fine tuning has been regarded as a de facto approach for adapting large language models (LLMs) to downstream tasks, but the high training memory consumption inherited from LLMs mak…
CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models
Yuefei Chen, Vivek K. Singh, Jing Ma +1
Counterfactual reasoning is widely recognized as one of the most challenging and intricate aspects of causality in artificial intelligence. In this paper, we evaluate the performan…