What will my model forget? forecasting forgotten examples in language model refinement

Turnitin 降AI改写 早检测系统 早降重系统 Turnitin-UK版 万方检测-期刊版 维普编辑部版 Grammarly检测 Paperpass检测 checkpass检测 PaperYY检测
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Unleashing the power of data tsunami: A comprehensive survey on data assessment and selection for instruction tuning of language models

Y Qin, Y Yang, P Guo, G Li, H Shao, Y Shi, Z Xu… - arxiv preprint arxiv …, 2024 - arxiv.org
Instruction tuning plays a critical role in aligning large language models (LLMs) with human
preference. Despite the vast amount of open instruction datasets, naively training a LLM on …
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BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization

G Lee, M Jeong, Y Kim, H Jung, J Oh, S Kim… - arxiv preprint arxiv …, 2024 - arxiv.org
While learning to align Large Language Models (LLMs) with human preferences has shown
remarkable success, aligning these models to meet the diverse user preferences presents …
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Demystifying Language Model Forgetting with Low-Rank Example Associations

X **, X Ren - arxiv preprint arxiv:2406.14026, 2024 - arxiv.org
Large Language models (LLMs) suffer from forgetting of upstream data when fine-tuned.
Despite efforts on mitigating forgetting, few have investigated whether, and how forgotten …
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