Papers by Jae Hee Lee
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic (2024.lrec-main)
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| Challenge: | Experimental evaluations of large language models demonstrate the efficacy of enhanced reasoning by logic. |
| Approach: | They propose a framework that uses symbolic logic to verify and rectify reasoning steps by steps. |
| Outcome: | The proposed framework improves the zero-shot chain-of-thought reasoning ability of large language models by verifying and rectifying the reasoning steps step by step. |
Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly used for knowledge-based reasoning tasks, yet understanding when they rely on genuine knowledge versus superficial heuristics remains challenging. |
| Approach: | They propose to ask LLMs to compare numerical attributes to find out which country has the highest population, France or Germany. |
| Outcome: | The proposed model comparisons show that heuristics override principled reasoning for larger models, while smaller models show no discrimination. |