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Attention Sensitivity Insufficient for In-Context Learning Diagnostics.

Jinyuan Zhang, Peng He, He Hu, Yin Yuan, ShengShuo Jiao· September 2, 2026 View original

Key takeaways

  • Attention sensitivity alone is an unreliable proxy for In-Context Learning in LLMs.
  • Fine-tuning can lead to a "Goodhart dissociation" where attention metrics improve but behavioral ICL does not.
  • Behavioral ICL metrics are crucial for accurate assessment of model adaptation.
  • Developers should validate attention-level proxies against actual task performance.

Who benefits

AI DevelopmentSoftware EngineeringResearch & DevelopmentEdTech

Summary

This paper demonstrates that attention-level metrics alone are insufficient to diagnose In-Context Learning (ICL) in fine-tuned LLMs, as models can show high attention sensitivity without actual behavioral ICL, leading to a "Goodhart dissociation." It proposes validating attention proxies against behavioral gaps.

Large Language Models (LLMs) exhibit In-Context Learning (ICL), allowing them to adapt to new tasks from examples provided within the prompt. However, fine-tuning can sometimes degrade this crucial capability. A common diagnostic approach involves checking if the model's attention patterns change when demonstrations in the prompt are altered, assuming this indicates context sensitivity. This research challenges the reliability of attention-level proxies for ICL. The authors formalize "In-Context Sensitivity" (ICS) based on attention differences and compare it with "ICL-GAP," a behavioral accuracy metric. Their experiments on Llama-2-7B show that a regularizer designed to maximize ICS can make attention highly context-sensitive, yet the model's actual behavioral ICL performance (ICL-GAP) remains poor, and overall accuracy drops significantly. The findings suggest a "Goodhart dissociation," where optimizing a proxy (attention sensitivity) does not lead to the desired outcome (behavioral ICL). The study concludes that attention-level ICL proxies should only be used as training targets after rigorous validation against actual behavioral performance metrics.

Why it matters

For professionals developing and fine-tuning LLMs, relying solely on attention-based metrics for ICL can be misleading, potentially leading to models that appear context-aware but fail to perform tasks effectively. This highlights the need for robust behavioral evaluation.

How to implement this in your domain

  1. 1Prioritize behavioral ICL metrics (e.g., ICL-GAP) over attention-level proxies when evaluating fine-tuned LLMs.
  2. 2Design fine-tuning strategies that explicitly aim to preserve or enhance behavioral ICL, not just attention sensitivity.
  3. 3Implement random-label protocols during evaluation to ensure the dynamic range of behavioral probes.
  4. 4Investigate the specific mechanisms of attention routing in fine-tuned models to understand where attention is directed.

Original post by Jinyuan Zhang, Peng He, He Hu, Yin Yuan, ShengShuo Jiao

"arXiv:2609.00064v1 Announce Type: new Abstract: In-context learning (ICL) lets large language models adapt to new tasks from demonstrations, and fine-tuning can erode this behaviour. Many preservation diagnostics inspect attention: if attention changes when demonstrations change,…"

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Originally posted by Jinyuan Zhang, Peng He, He Hu, Yin Yuan, ShengShuo Jiao on X · view source

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