Papers with symbolic module
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning (2024.eacl-long)
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Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell, Kartik Talamadupula, Tim Klinger
| Challenge: | Text-based games (TBGs) combine natural language understanding with reasoning. |
| Approach: | They propose an exploration-guided reasoning agent for textual reinforcement learning that integrates natural language with reasoning. |
| Outcome: | The proposed agent outperforms baseline agents on TWG and TWC games. |
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming (2023.findings-acl)
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| Challenge: | Pre-trained large language models struggle to perform logical reasoning reliably despite advances in scale and compositionality. |
| Approach: | They propose a Differentiable Symbolic Reasoning framework that uses symbolic programming to improve LMs' logical reasoning abilities. |
| Outcome: | The proposed framework outperforms competitive baselines when faced with systematic changes in sequence length. |
Multi-modal Action Chain Abductive Reasoning (2023.acl-long)
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Mengze Li, Tianbao Wang, Jiahe Xu, Kairong Han, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Shiliang Pu, Fei Wu
| Challenge: | Existing models for Abductive Reasoning are limited in their ability to infer the most plausible explanation of incomplete known phenomena. |
| Approach: | They propose a vision-language task that aims to imagine the most plausible event by spatio-temporal grounding in past video and infer the hypothesis of subsequent action chain layer by layer. |
| Outcome: | The proposed model outperforms existing video-language models in terms of effectiveness on the proposed dataset. |