Preprocessing Choices in Fine-Tuning Data Matter: Utility and Demographic Sensitivity in LLM-Based Recommendation

21 hours agoReported by arXiv36

Preprocessing Choices in Fine-Tuning Data Matter: Utility and Demographic Sensitivity in LLM-Based Recommendation. The story centres on Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Funding and AI Research — the section covering rounds, valuations, acquisitions and investor moves in AI.

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Reported by arXiv

First seen 21 hours ago

arXivPreprocessing Choices in Fine-Tuning Data Matter: Utility and Demographic Sensitivity in LLM-Based Recommendation21 hours agoFurkan Yasir Goksu

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Topology-Stratified Materials Discovery with A Flow-Based Generative Model

Topology-Stratified Materials Discovery with A Flow-Based Generative Model. It centres on Benchmarks, and also names Fine-tuning. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.

arXiv22 Sept 2026

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Preprocessing Choices in Fine-Tuning Data Matter: Utility and Demographic Sensitivity in LLM-Based Recommendation

Companies, models and people in this story

Reported by arXiv

Topology-Stratified Materials Discovery with A Flow-Based Generative Model

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation

On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery

Companies, models and people in this story

Reported by arXiv

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On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

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