ENTP: Enhancing Low-Quality SFT Data via Neural-Symbolic Text Purge-Mix

• URL: http://arxiv.org/abs/2510.23160v1
• Date: Mon, 27 Oct 2025 09:39:22 GMT
• Title: ENTP: Enhancing Low-Quality SFT Data via Neural-Symbolic Text Purge-Mix
• Abstract summary: Supervised Fine-Tuning (SFT) adapts pre-trained Large Language Models (LLMs) to domain-specific instructions by training on a subset of high-quality instruction-response pairs.<n>Existing quality-first paradigms often overlook valuable signals in discarded low-quality data and rely on imperfect quality filters.<n>We introduce ENTP (Enhancing low-quality SFT data via Neural-symbolic Text Purge-Mix), a framework that revitalizes low-quality corpora through symbolic purification and neural reconstruction.
• Score: 31.617919238979013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervised Fine-Tuning (SFT) adapts pre-trained Large Language Models (LLMs) to domain-specific instructions by training on a carefully curated subset of high-quality instruction-response pairs, typically drawn from a larger dataset that often contains many low-quality or noisy samples. However, existing quality-first paradigms often overlook valuable signals in discarded low-quality data and rely on imperfect quality filters. We introduce ENTP (Enhancing low-quality SFT data via Neural-symbolic Text Purge-Mix), a framework that revitalizes low-quality corpora through symbolic purification and neural reconstruction. The symbolic module identifies and prunes noisy samples based on statistical priors, while the neural component synthesizes enriched instruction-response pairs by leveraging latent representations and model knowledge. This neural-symbolic synergy enhances data informativeness and diversity. Experiments show that ENTP-augmented datasets, constructed exclusively from low-quality data, outperform 13 established data-selection baselines across five instruction-following benchmarks, and even surpass fine-tuning on the full original dataset (approximately 300K examples). Our results highlight the untapped potential of low-quality data and underscore the importance of intelligent purification and synthesis for efficient instruction alignment.

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