For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs

Wenlong Deng, Qi Zeng, Jiaming Zhang, Minghui Chen, Zixin Ding, Christos Thrampoulidis, Boying Gong, Xiaoxiao Li in ACL

Authors: Wenlong Deng, Qi Zeng, Jiaming Zhang, Minghui Chen, Zixin Ding, Christos Thrampoulidis, Boying Gong, Xiaoxiao Li

Published in: Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers (ACL 2026), July 2026, pp. 14581–14600

Overview

For-Value estimates data influence using final-layer representations and prediction errors. A closed-form calculation replaces backpropagation, allowing examples to be evaluated in batches with a single forward pass. The paper evaluates the method on influential-data detection and mislabeled-data identification for language and vision-language models.

Posted on:: July 1, 2026

Length:: 1 minute read, 85 words

Categories:: ACL

Tags:: Data Valuation Large Language Models Vision-Language Models Fine-Tuning

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