@inproceedings{wang-etal-2025-rescorla,
title = "Rescorla-Wagner Steering of {LLM}s for Undesired Behaviors over Disproportionate Inappropriate Context",
author = "Wang, Rushi and
Liu, Jiateng and
Qian, Cheng and
Shen, Yifan and
Pan, Yanzhou and
Xu, Zhaozhuo and
Abbasi, Ahmed and
Ji, Heng and
Zhang, Denghui",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1003/",
doi = "10.18653/v1/2025.emnlp-main.1003",
pages = "19810--19845",
ISBN = "979-8-89176-332-6",
abstract = "Incorporating external context can significantly enhance the response quality of Large Language Models (LLMs). However, real-world contexts often mix relevant information with disproportionate inappropriate content, posing reliability risks. How do LLMs process and prioritize mixed context? To study this, we introduce the Poisoned Context Testbed, pairing queries with real-world contexts containing relevant and inappropriate content. Inspired by associative learning in animals, we adapt the Rescorla-Wagner (RW) model from neuroscience to quantify how competing contextual signals influence LLM outputs. Our adapted model reveals a consistent behavioral pattern: LLMs exhibit a strong tendency to incorporate information that is less prevalent in the context. This susceptibility is harmful in real-world settings, where small amounts of inappropriate content can substantially degrade response quality. Empirical evaluations on our testbed further confirm this vulnerability. To tackle this, we introduce RW-Steering, a two-stage finetuning-based approach that enables the model to internally identify and ignore inappropriate signals. Unlike prior methods that rely on extensive supervision across diverse context mixtures, RW-Steering generalizes robustly across varying proportions of inappropriate content. Experiments show that our best fine-tuned model improves response quality by 39.8{\%} and reverses the undesirable behavior curve, establishing RW-Steering as a robust, generalizable solution for improving LLM safety in real-world use."
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<abstract>Incorporating external context can significantly enhance the response quality of Large Language Models (LLMs). However, real-world contexts often mix relevant information with disproportionate inappropriate content, posing reliability risks. How do LLMs process and prioritize mixed context? To study this, we introduce the Poisoned Context Testbed, pairing queries with real-world contexts containing relevant and inappropriate content. Inspired by associative learning in animals, we adapt the Rescorla-Wagner (RW) model from neuroscience to quantify how competing contextual signals influence LLM outputs. Our adapted model reveals a consistent behavioral pattern: LLMs exhibit a strong tendency to incorporate information that is less prevalent in the context. This susceptibility is harmful in real-world settings, where small amounts of inappropriate content can substantially degrade response quality. Empirical evaluations on our testbed further confirm this vulnerability. To tackle this, we introduce RW-Steering, a two-stage finetuning-based approach that enables the model to internally identify and ignore inappropriate signals. Unlike prior methods that rely on extensive supervision across diverse context mixtures, RW-Steering generalizes robustly across varying proportions of inappropriate content. Experiments show that our best fine-tuned model improves response quality by 39.8% and reverses the undesirable behavior curve, establishing RW-Steering as a robust, generalizable solution for improving LLM safety in real-world use.</abstract>
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%0 Conference Proceedings
%T Rescorla-Wagner Steering of LLMs for Undesired Behaviors over Disproportionate Inappropriate Context
%A Wang, Rushi
%A Liu, Jiateng
%A Qian, Cheng
%A Shen, Yifan
%A Pan, Yanzhou
%A Xu, Zhaozhuo
%A Abbasi, Ahmed
%A Ji, Heng
%A Zhang, Denghui
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wang-etal-2025-rescorla
%X Incorporating external context can significantly enhance the response quality of Large Language Models (LLMs). However, real-world contexts often mix relevant information with disproportionate inappropriate content, posing reliability risks. How do LLMs process and prioritize mixed context? To study this, we introduce the Poisoned Context Testbed, pairing queries with real-world contexts containing relevant and inappropriate content. Inspired by associative learning in animals, we adapt the Rescorla-Wagner (RW) model from neuroscience to quantify how competing contextual signals influence LLM outputs. Our adapted model reveals a consistent behavioral pattern: LLMs exhibit a strong tendency to incorporate information that is less prevalent in the context. This susceptibility is harmful in real-world settings, where small amounts of inappropriate content can substantially degrade response quality. Empirical evaluations on our testbed further confirm this vulnerability. To tackle this, we introduce RW-Steering, a two-stage finetuning-based approach that enables the model to internally identify and ignore inappropriate signals. Unlike prior methods that rely on extensive supervision across diverse context mixtures, RW-Steering generalizes robustly across varying proportions of inappropriate content. Experiments show that our best fine-tuned model improves response quality by 39.8% and reverses the undesirable behavior curve, establishing RW-Steering as a robust, generalizable solution for improving LLM safety in real-world use.
%R 10.18653/v1/2025.emnlp-main.1003
%U https://aclanthology.org/2025.emnlp-main.1003/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1003
%P 19810-19845
Markdown (Informal)
[Rescorla-Wagner Steering of LLMs for Undesired Behaviors over Disproportionate Inappropriate Context](https://aclanthology.org/2025.emnlp-main.1003/) (Wang et al., EMNLP 2025)
ACL
- Rushi Wang, Jiateng Liu, Cheng Qian, Yifan Shen, Yanzhou Pan, Zhaozhuo Xu, Ahmed Abbasi, Heng Ji, and Denghui Zhang. 2025. Rescorla-Wagner Steering of LLMs for Undesired Behaviors over Disproportionate Inappropriate Context. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 19810–19845, Suzhou, China. Association for Computational Linguistics.