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Source note3 min read

Diagnosing Task Insensitivity in Language Agents

Source

arXiv

arxiv.org/abs/2606.26918v1

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Source

Plain-Language Brief

The research discusses a problem with large language models where they struggle to adapt to new tasks outside of their original training data (OOD generalization). The authors call this issue 'task insensitivity'. They find that models continue to apply patterns learned during training even when the task is semantically altered, which can lead to incorrect outputs. The authors propose a solution called Task-Perturbed NLL Optimization, a lightweight regularizer that encourages the model to pay more attention to the task instruction and improves task sensitivity.

What the Document Says

  • Large language models struggle with out-of-distribution (OOD) generalization due to task insensitivity.
  • Models may continue with actions aligned with the original task even when the instruction is semantically corrupted.
  • Replacing the task description in a trained prompt with another similar but distinct task may lead the model to output the same action.
  • This behavior suggests an optimization bias toward shortcuts and a drift away from task tokens in attention.
  • The authors propose Task-Perturbed NLL Optimization as a solution to improve task sensitivity.
  • Extensive evaluations show that this intervention improves task sensitivity and OOD generalization while preserving stable attention to task tokens.

Key Concepts Mentioned

  • large language models
  • out-of-distribution (OOD) generalization
  • task insensitivity
  • task tokens
  • attention drift
  • Task-Perturbed NLL Optimization

Claims Made by the Document

  • Large language models struggle with OOD generalization due to task insensitivity.
  • The proposed solution, Task-Perturbed NLL Optimization, improves task sensitivity and OOD generalization.
  • The solution preserves stable attention to task tokens.

Evidence Used

  • Results from the study show improvements in task sensitivity and OOD generalization after implementing the proposed solution.
  • The text describes the behavior of large language models that continues with actions aligned with the original task even when the instruction is semantically altered, which leads to incorrect outputs.

Methods / Approach

  • Task-Perturbed NLL Optimization

Limitations Stated by the Authors

  • The study focuses on improving task sensitivity in large language models, but other limitations or potential improvements may exist.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • As a model, I don't have the ability to conduct new research or experiments. My responses are based solely on the information provided in the text.

Open Questions

  • What other potential limitations or improvements could exist for large language models in terms of task sensitivity and OOD generalization?
  • How does Task-Perturbed NLL Optimization compare to other methods aimed at improving task sensitivity in large language models?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The research focuses on a specific problem with large language models and proposes a solution. However, there may be additional limitations or potential improvements not addressed in the study.

Raw Abstract / Excerpt

Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak. We identify a key source of this failure as task insensitivity: when faced with similar but distinct tasks, models might apply patterns learned during training and fail to solve the task at hand. We show that models often continue with actions aligned with the original task even when the instruction is semantically corrupted and cannot be directly answered. We further find that, when we replace the task description in a trained prompt with another similar but distinct task, the model may still output the same action. This behavior is accompanied by a consistent training-time attention drift away from task tokens and toward local observations, suggesting an optimization bias toward shortcuts. To mitigate this problem, we propose Task-Perturbed NLL Optimization, a lightweight contrastive regularizer that explicitly encourages action dependence on the task instruction. Extensive evaluations show that our intervention improves task sensitivity and OOD generalization while preserving more stable attention to task tokens.