Papers by Liwei Kang
On the Empirical Complexity of Reasoning and Planning in LLMs (2024.findings-emnlp)
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| Challenge: | Evidence shows that the relative performance of CoT, ToT, and their variants may vary from task to task. |
| Approach: | They propose to use chain-of-thought (CoT), tree-of thought (ToT), and related techniques to solve complex reasoning tasks with Large Language Models. |
| Outcome: | The proposed methods outperform the linear structure of CoT on hard reasoning tasks. |
When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models (2024.acl-long)
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| Challenge: | Existing clustering-based open relation extraction methods use pre-trained language models . embeddings from language models are high-dimensional and anisotropic, so there is a gap . |
| Approach: | They propose a framework that makes two LLMs work collaboratively to achieve clustering. |
| Outcome: | The proposed framework outperforms existing methods by 1.4%3.13% on different datasets. |