Papers by Gael Gendron
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)
Copied to clipboard
Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
| Challenge: | Empirical evidence shows that our proposed method improves performance across seven downstream tasks. |
| Approach: | They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data. |
| Outcome: | The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks. |
Can Large Language Models Learn Independent Causal Mechanisms? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) perform poorly on complex reasoning tasks, such as abstract, causal, or logical reasoning. |
| Approach: | They propose to use two concepts from causality to learn ICMs within LLMs to improve out-of-distribution performance on abstract and causal reasoning tasks. |
| Outcome: | The proposed model outperforms existing models on abstract and causal reasoning tasks and is more robust to fine-tuning. |