---
id: 20261001-T0-13
title: "LLM微调一定要人类可读文本吗？研究发现模型条件化表示也能有效适配"
title_en: "Study Asks: Is Human-Readable Text Necessary for Effective LLM Fine-Tuning?"
url: https://ai.daily.yangsir.net/daily/20261001-T0-13
issue_date: 2026-10-01
publish_date: 2026-09-30T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2609.35868
---

# LLM微调一定要人类可读文本吗？研究发现模型条件化表示也能有效适配

一篇新论文探讨了人类可读性对LLM微调是否必要。研究者提出使用模型条件化的训练表示，在不要求人类可读文本形式的前提下，检验能否保持甚至提升模型的适配效果。这意味着未来微调可能不再依赖纯文本格式的数据，而是使用模型自身能理解的条件化表示。如果结论成立，将为微调数据的表示形式打开新空间，降低对人工标注和文本清洗的依赖，对需要高效适配大模型的团队有直接参考价值。

## English Version

**Study Asks: Is Human-Readable Text Necessary for Effective LLM Fine-Tuning?**

A new paper investigates whether human readability is necessary for effective fine-tuning of large language models. The researchers propose using model-conditioned training representations to test whether adaptation utility can be preserved or improved without requiring a human-readable textual form. If the findings hold, fine-tuning could move beyond plain-text data toward representations the model itself understands. This would open new possibilities for fine-tuning data formats, reduce reliance on manual annotation and text cleaning, and offer direct reference for teams needing efficient LLM adaptation.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2609.35868)

**详情页**：https://ai.daily.yangsir.net/daily/20261001-T0-13

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