Pushing the boundary on Natural Language Inference

• URL: http://arxiv.org/abs/2504.18376v1
• Date: Fri, 25 Apr 2025 14:20:57 GMT
• Title: Pushing the boundary on Natural Language Inference
• Authors: Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín, David Camacho,
• Abstract summary: Natural Language Inference (NLI) is a central task in natural language understanding with applications in fact-checking, question answering and information retrieval.<n>Despite its importance, current NLI systems heavily rely on learning with limiting artifacts and biases, inference and real-world applicability.<n>This work provides a framework for building robust NLI systems without sacrificing quality or real-world applicability.
• Score: 49.15148871877941
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Natural Language Inference (NLI) is a central task in natural language understanding with applications in fact-checking, question answering, and information retrieval. Despite its importance, current NLI systems heavily rely on supervised learning with datasets that often contain annotation artifacts and biases, limiting generalization and real-world applicability. In this work, we apply a reinforcement learning-based approach using Group Relative Policy Optimization (GRPO) for Chain-of-Thought (CoT) learning in NLI, eliminating the need for labeled rationales and enabling this type of training on more challenging datasets such as ANLI. We fine-tune 7B, 14B, and 32B language models using parameter-efficient techniques (LoRA and QLoRA), demonstrating strong performance across standard and adversarial NLI benchmarks. Our 32B AWQ-quantized model surpasses state-of-the-art results on 7 out of 11 adversarial sets$\unicode{x2013}$or on all of them considering our replication$\unicode{x2013}$within a 22GB memory footprint, showing that robust reasoning can be retained under aggressive quantization. This work provides a scalable and practical framework for building robust NLI systems without sacrificing inference quality.

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