Papers by Caoyun Fan
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning. (2022.coling-1)
Copied to clipboard
| Challenge: | Reasoning and knowledge-related skills are considered as fundamental skills for natural language understanding (NLU) tasks. |
| Approach: | They propose a method to diagnose correlations between an NLU dataset and a specific skill. |
| Outcome: | The proposed method is able to diagnose correlations between dataset and logical reasoning skill on 8 MRC and 3 NLI datasets. |
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding (2023.emnlp-main)
Copied to clipboard
| Challenge: | Chain-of-Thought (CoT) is a technique that guides large language models to decompose complex tasks into multi-step reasoning processes. |
| Approach: | They propose a two-step reasoning framework based on prompt tuning to implement step-by-step thinking for MLMs on NLU tasks. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art performance on two NLU tasks. |
MTR: A Dataset Fusing Inductive, Deductive, and Defeasible Reasoning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for logical reasoning focus on monotonic logic and a single form of reasoning. |
| Approach: | They propose to use a dataset to study the human-like reasoning in machine reading comprehension. |
| Outcome: | The proposed dataset shows that state-of-the-art neural models perform noticeably worse than expected. |