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LLM Explanations Often Insufficient, Research Finds

Nhi Nguyen, Shauli Ravfogel, Rajesh Ranganath· June 30, 2026 View original

Summary

This research introduces a new metric, SCSuff, to evaluate the sufficiency of free-text explanations from large language models (LLMs) by assessing if explanations contain enough information to justify model outputs based on the LLM's own input beliefs. Experiments show that LLM explanations are generally insufficient and their sufficiency varies with input distribution, with weak correlation to model size or accuracy.

As large language models are increasingly used in critical applications, the reliability of their free-text explanations, such as chain-of-thought, becomes paramount. This paper investigates whether these explanations truly provide enough information to understand the model's output-generating process. Researchers propose a novel information-theoretic metric called SCSuff (Self-Consistent Sufficiency) to evaluate explanation sufficiency. This metric leverages the LLM itself to generate alternative inputs conditioned on an explanation, effectively capturing the model's internal beliefs about possible inputs. Experiments reveal that LLM explanations are often insufficient, meaning they don't fully capture the model's reasoning. The sufficiency also depends on the input data distribution and shows little correlation with model size, accuracy, or output entropy. The study suggests that internal representations can predict SCSuff scores, potentially guiding improvements in explanation quality.

Why it matters

For professionals deploying LLMs in high-stakes environments, understanding the limitations of model explanations is crucial for building trust, ensuring accountability, and mitigating risks associated with potentially misleading rationales.

How to implement this in your domain

  1. 1Integrate explanation sufficiency metrics like SCSuff into LLM evaluation pipelines.
  2. 2Prioritize research and development into methods that improve the self-consistency of LLM explanations.
  3. 3Educate stakeholders on the current limitations of LLM explanations, especially in critical applications.
  4. 4Develop human-in-the-loop processes to validate LLM explanations where high stakes are involved.

Who benefits

HealthcareBFSILegalAI/ML DevelopmentCybersecurity

Key takeaways

  • LLM free-text explanations are often insufficient to fully justify model outputs.
  • A new metric, SCSuff, evaluates explanation sufficiency based on the LLM's own input beliefs.
  • Explanation sufficiency varies with input distribution and is weakly correlated with model size or accuracy.
  • Internal model states may help predict and improve explanation quality.

Original post by Nhi Nguyen, Shauli Ravfogel, Rajesh Ranganath

"arXiv:2606.28615v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, where free-text explanations such as chain-of-thought and post-hoc rationales are used to justify model outputs. Yet it remains unclear whether these exp…"

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Primary sources

Originally posted by Nhi Nguyen, Shauli Ravfogel, Rajesh Ranganath on X · view source

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