LLMs Should Not Yet Be Credited with Decision Explanation
In a recent position paper published on arXiv, researchers argue against granting Large Language Models (LLMs) the status of providing genuine decision explanations. This assertion comes in light of growing evidence that suggests LLMs can predict behaviors and generate rationales, but the authors caution that these capabilities do not equate to understanding the underlying reasons behind human decision-making.
Understanding the Distinction
The paper distinguishes between three key claims regarding LLMs:
- Decision Prediction: The ability of LLMs to forecast human choices based on previous data.
- Rationale Generation: The capacity to create plausible explanations for why certain decisions are made.
- Decision Explanation: The actual understanding of the reasoning process that leads to a decision.
The authors contend that while LLMs often excel in the first two claims—predicting decisions and generating rationales—their performance does not satisfactorily meet the criteria for genuine decision explanation.
Evidence and Their Limitations
The researchers note that much of the evidence presented in support of LLM-based decision accounts tends to reinforce the first two claims without sufficiently addressing the nuances of decision explanation. Specifically, they point out that:
- Predictions can be made without understanding the contextual factors that influence human behavior.
- Rationales provided by LLMs may merely reflect surface-level associations rather than deeper cognitive processes.
- Outcome-conditioned reasoning traces, while useful, do not inherently clarify the ‘why’ behind decisions.
The authors argue that equating predictive success with explanatory power risks a redefinition of what constitutes meaningful progress in understanding human decision-making.
A Bridge Standard for Decision-Explanation Credit
To address these shortcomings, the paper proposes a bridge standard for assessing when LLMs should be credited for decision explanations. The authors suggest that stronger claims should meet several criteria:
- Specify Explanatory Targets: Clearly define what is being explained.
- Discriminate Against Weaker Rationalizer Alternatives: Show that the model’s explanation is superior to simpler rationalizations.
- Use Target-Appropriate Validation: Employ validation methods that are sensitive to the specific processes or interventions involved.
- Bound Their Scope: Limit claims to specific contexts to avoid overgeneralization.
The authors emphasize that this approach not only preserves the value of LLMs as effective predictors and hypothesis generators but also mitigates the risks of prematurely attributing explanatory capabilities to them.
Conclusion: A Principle of Credit Calibration
The paper concludes with a principle of credit calibration, asserting that LLMs should only receive credit for the strongest claims their evidence can support. By adopting this principle, researchers can better leverage LLMs as tools for discovering, testing, and communicating explanations of human behavior, rather than merely as persuasive narrators of decisions.
This nuanced perspective urges the AI research community to tread carefully in attributing explanatory capabilities to LLMs, highlighting the importance of rigorous standards in evaluating their contributions to understanding human decision-making.
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