Papers by Willis Guo
Verifiable, Debuggable, and Repairable Commonsense Logical Reasoning via LLM-based Theory Resolution (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have led to substantial interest in their application to commonsense reasoning tasks. |
| Approach: | They propose a logical reasoning framework that integrates commonsense knowledge with a verifiable logical framework that mitigates hallucinations and facilitates debugging. |
| Outcome: | The proposed framework improves on three language-based reasoning tasks and improves accuracy and reasoning correctness. |
Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering (2024.emnlp-main)
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| Challenge: | Existing Knowledge Graph Question Answering (KGQA) methods focus on answering factual questions, leaving questions involving commonsense reasoning unaddressed. |
| Approach: | They propose a commonsense KGQA methodology that axiomatically surfaces commonsensical knowledge of Large Language Models and grounding every factual reasoning step on KG triples. |
| Outcome: | The proposed method outperforms existing methods and reduces instances of hallucination and reasoning errors. |