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Qinkai Yu

2 accepted papers

2025

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

COLING 2025main

Large language models (LLMs) have shown remarkable performances across a wide range of tasks. However, the mechanisms by which these models encode tasks of varying complexities remain poorly understood. In this paper, we explore the hypothesis that LLMs process concepts of varying complexities in di…

2024

The Impact of Reasoning Step Length on Large Language Models

ACL 2024findings

Chain of Thought (CoT) is significant in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length of reasoning steps in prompts remains largely unknown. To shed light on this, we have conducted several empirical exper…

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