Counterfactual Samples Constructing and Training for Commonsense Statements Estimation

• URL: http://arxiv.org/abs/2412.20563v1
• Date: Sun, 29 Dec 2024 20:18:52 GMT
• Title: Counterfactual Samples Constructing and Training for Commonsense Statements Estimation
• Authors: Chong Liu, Zaiwen Feng, Lin Liu, Zhenyun Deng, Jiuyong Li, Ruifang Zhai, Debo Cheng, Li Qin,
• Abstract summary: Plausibility Estimation plays a crucial role for enabling language models to objectively comprehend the real world. They lack two key traits of an ideal PE model: language-explainable and commonsense-sensitive. We propose a novel model-agnostic method, referred to as Commonsense Counterfactual Samples Generating.
• Score: 17.970740197590693
• License:
• Abstract: Plausibility Estimation (PE) plays a crucial role for enabling language models to objectively comprehend the real world. While large language models (LLMs) demonstrate remarkable capabilities in PE tasks but sometimes produce trivial commonsense errors due to the complexity of commonsense knowledge. They lack two key traits of an ideal PE model: a) Language-explainable: relying on critical word segments for decisions, and b) Commonsense-sensitive: detecting subtle linguistic variations in commonsense. To address these issues, we propose a novel model-agnostic method, referred to as Commonsense Counterfactual Samples Generating (CCSG). By training PE models with CCSG, we encourage them to focus on critical words, thereby enhancing both their language-explainable and commonsense-sensitive capabilities. Specifically, CCSG generates counterfactual samples by strategically replacing key words and introducing low-level dropout within sentences. These counterfactual samples are then incorporated into a sentence-level contrastive training framework to further enhance the model's learning process. Experimental results across nine diverse datasets demonstrate the effectiveness of CCSG in addressing commonsense reasoning challenges, with our CCSG method showing 3.07% improvement against the SOTA methods.

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