Symbolic Working Memory Enhances Language Models for Complex Rule Application (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel in single-step rule application but struggle with multi-step deductive reasoning when rules are presented non-sequentially. |
| Approach: | They propose to augment LLMs with external working memory and introduce a neurosymbolic framework for rule application that stores facts and rules in both natural language and symbolic forms, enabling precise tracking. |
| Outcome: | The proposed framework iteratively performs symbolic rule grounding and LLM-based rule implementation. |
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