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This article is primarily about a novel selective fine-tuning method for large language models, making it central to the field of AI research despite its subsequent retraction due to experimental flaws. The proposed Adjacent Possible Exploration (APE) framework aims to balance performance gains with model stability through an acceptance criterion process, though the reported results are now invalid.
APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation
arXiv:2505.19912v3 Announce Type: replace Abstract: We present Adjacent Possible Exploration (APE), a selective fine-tuning method for adapting large language models that systematically explores parameter modifications while maintaining model stability. Inspired by evolutionary optimization principles, APE evaluates multiple candidate parameter updates through fine-tuning on small data subsets and accepts only those exceeding a performance threshold. Unlike standard fine-tuning that follows single gradient directions, APE implements a filtered selection process that prevents destabilizing parameter changes while enabling systematic improvement. Our method achieves 33.9\% BLEU improvement and 36.2\% perplexity reduction on news summarization tasks while using minimal computational resources. The approach provides a practical framework for controlled model adaptation that balances performance gains with representational stability.
Abstract:We present Adjacent Possible Exploration (APE), a selective fine-tuning method for adapting large language models that systematically explores parameter modifications while maintaining model stability. Inspired by evolutionary optimization principles, APE evaluates multiple candidate parameter updates through fine-tuning on small data subsets and accepts only those exceeding a performance threshold. Unlike standard fine-tuning that follows single gradient directions, APE implements a filtered selection process that prevents destabilizing parameter changes while enabling systematic improvement. Our method achieves 33.9\% BLEU improvement and 36.2\% perplexity reduction on news summarization tasks while using minimal computational resources. The approach provides a practical framework for controlled model adaptation that balances performance gains with representational stability.
Withdrawn by the author. The experiments reported in Section 3 (Table 3, Figure 2) do not implement the acceptance criterion described in Section 2, and the human evaluation in Section 3.2 is not valid. The reported results and conclusions should be disregarded
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2505.19912 [cs.CL]
(or arXiv:2505.19912v3 [cs.CL] for this version)
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