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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

Researchers identify an issue with large language models where they are unable to adapt to new tasks effectively due to their task insensitivity. The authors propose a lightweight solution called Task-Perturbed NLL Optimization to improve task sensitivity and out-of-distribution generalization.

What the Document Says

  • The research discusses the weak out-of-distribution (OOD) generalization of large language models serving as long-horizon agents.
  • The authors point out that task insensitivity is a key cause, where models continue with actions learned during training even when faced with new or corrupted tasks.
  • They find that the model may still output the same action when the task description is replaced with another similar but distinct task.
  • A consistent training-time attention drift away from task tokens and toward local observations indicates an optimization bias toward shortcuts.
  • The authors propose Task-Perturbed NLL Optimization, a lightweight contrastive regularizer to encourage action dependence on the task instruction.
  • 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
  • attention drift
  • Task-Perturbed NLL Optimization

Claims Made by the Document

  • Large language models have weak OOD generalization.
  • Task insensitivity is a key cause of this weakness.
  • Models may continue with actions learned during training even when faced with new or corrupted tasks.
  • The model's output may remain the same when the task description is replaced with another similar but distinct task.
  • A consistent attention drift away from task tokens and toward local observations indicates an optimization bias toward shortcuts.
  • Task-Perturbed NLL Optimization improves task sensitivity and OOD generalization while preserving stable attention to task tokens.

Evidence Used

  • The text discusses the weak OOD generalization of large language models and the problem of task insensitivity.
  • The authors provide examples where models fail to solve new tasks due to their reliance on patterns learned during training.
  • They observe a consistent attention drift away from task tokens and toward local observations.
  • Evaluations are conducted to test the effectiveness of Task-Perturbed NLL Optimization.

Methods / Approach

  • Study and analysis of large language models' behavior
  • Proposal of Task-Perturbed NLL Optimization
  • Extensive evaluations

Limitations Stated by the Authors

The study focuses on a specific issue with large language models and proposes a solution. However, it may not cover all potential problems or solutions related to these models.

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 evaluations. The limitations mentioned in 'limitations_authors' are based on the information provided in the source text.

Open Questions

  • What other causes might contribute to weak OOD generalization in large language models?
  • Are there other potential solutions to improve task sensitivity and OOD generalization?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text does not provide information on what specific large language models were used for the study, how the evaluations were conducted, or the exact nature of Task-Perturbed NLL Optimization.

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.