Mitigating Spurious Correlations in NLI via LLM-Synthesized Counterfactuals and Dynamic Balanced Sampling

• URL: http://arxiv.org/abs/2512.18462v1
• Date: Sat, 20 Dec 2025 18:30:54 GMT
• Title: Mitigating Spurious Correlations in NLI via LLM-Synthesized Counterfactuals and Dynamic Balanced Sampling
• Authors: Christopher Román Jaimes,
• Abstract summary: Natural Language Inference (NLI) models frequently rely on spurious correlations rather than semantic reasoning.<n>Existing mitigation strategies often incur high annotation costs or trigger catastrophic forgetting during fine-tuning.<n>We propose an automated, scalable pipeline to address these limitations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural Language Inference (NLI) models frequently rely on spurious correlations rather than semantic reasoning. Existing mitigation strategies often incur high annotation costs or trigger catastrophic forgetting during fine-tuning. We propose an automated, scalable pipeline to address these limitations. First, we introduce Log-Frequency LMI (LF-LMI) to accurately detect semantic artifacts. Second, we generate a high-quality synthetic contrast set via an LLM-synthesis pipeline with multi-judge verification. Finally, we introduce Dynamic Balanced Sampling, a training strategy that rotates the original data distribution to prevent forgetting. Our method improves consistency on a challenging benchmark from 63.5% to 81.0% while maintaining 88.4% in-domain accuracy, significantly outperforming naive fine-tuning.

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