Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence
Quick summary
arXiv:2609.05385v2 Announce Type: replace Abstract: LLM decision components that can operate within agent workflows often produce action-relevant recommendations or judgements together with explanations. Operators may use the named factors to monitor a system, diagnose errors, or decide when to escalate an output. Such use assumes that the explanations agree with the component's observable decision behaviour. We test two interpretations of the named factors: necessity, meaning that changing a factor would change the output, and sufficiency, meaning that retaining it while removing other change
Key takeaways
- arXiv:2609.05385v2 Announce Type: replace Abstract: LLM decision components that can operate within agent workflows often produce action-relevant recommendations or judgements together with explanations.
- Operators may use the named factors to monitor a system, diagnose errors, or decide when to escalate an output.
- Such use assumes that the explanations agree with the component's observable decision behaviour.
Why it matters
“Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
Member comments