JSON schema constraints improve LLM extraction of nested quantum cascade laser structures to 83.4% F1, delivering up to 24.1% gains for smaller models.
The effect of fine-tuning on language model toxicity
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Benign fine-tuning of foundation models induces large, heterogeneous, and often contradictory changes in safety metrics across general and domain-specific benchmarks.
The paper proposes Dual-Reference SFT (DR-SFT) to defend LLMs against harmful QA pairs embedded in benign training samples, where existing guardrails fail at the example level.
Survey of harmful fine-tuning attacks on LLMs, their variants, defense strategies, mechanical analysis, and evaluation methodologies.
A survey that catalogs threat models, detection approaches, and mitigation strategies for toxicity in multilingual LLMs while identifying challenges such as uneven language coverage and culturally variable harm definitions.
citing papers explorer
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Information Extraction of Nested Complex Structure of Quantum Cascade Lasers via Large Language Models
JSON schema constraints improve LLM extraction of nested quantum cascade laser structures to 83.4% F1, delivering up to 24.1% gains for smaller models.
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Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains
Benign fine-tuning of foundation models induces large, heterogeneous, and often contradictory changes in safety metrics across general and domain-specific benchmarks.
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Defending Against Harmful Supervision Hidden in Benign Samples
The paper proposes Dual-Reference SFT (DR-SFT) to defend LLMs against harmful QA pairs embedded in benign training samples, where existing guardrails fail at the example level.
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Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
Survey of harmful fine-tuning attacks on LLMs, their variants, defense strategies, mechanical analysis, and evaluation methodologies.
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A Survey of Toxicity Detection and Mitigation Strategies for Multilingual Language Models
A survey that catalogs threat models, detection approaches, and mitigation strategies for toxicity in multilingual LLMs while identifying challenges such as uneven language coverage and culturally variable harm definitions.