Reasoning Elicitation in Language Models via Counterfactual Feedback

• URL: http://arxiv.org/abs/2410.03767v1
• Date: Wed, 2 Oct 2024 15:33:30 GMT
• Title: Reasoning Elicitation in Language Models via Counterfactual Feedback
• Authors: Alihan Hüyük, Xinnuo Xu, Jacqueline Maasch, Aditya V. Nori, Javier González,
• Abstract summary: We derive novel metrics that balance accuracy in factual and counterfactual questions. We propose several fine-tuning approaches that aim to elicit better reasoning mechanisms. We evaluate the performance of the fine-tuned language models in a variety of realistic scenarios.
• Score: 17.908819732623716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the increasing effectiveness of language models, their reasoning capabilities remain underdeveloped. In particular, causal reasoning through counterfactual question answering is lacking. This work aims to bridge this gap. We first derive novel metrics that balance accuracy in factual and counterfactual questions, capturing a more complete view of the reasoning abilities of language models than traditional factual-only based metrics. Second, we propose several fine-tuning approaches that aim to elicit better reasoning mechanisms, in the sense of the proposed metrics. Finally, we evaluate the performance of the fine-tuned language models in a variety of realistic scenarios. In particular, we investigate to what extent our fine-tuning approaches systemically achieve better generalization with respect to the base models in several problems that require, among others, inductive and deductive reasoning capabilities.

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