A recent paper on arXiv cs.AI introduces a test-time approach designed to enhance the faithfulness of explanations generated by large language models (LLMs). The method specifically addresses incompleteness, one of two identified dimensions of unfaithful explanations. This approach operates by removing uncredited concepts from the input and re-querying the model, aiming to eliminate unmentioned influences while retaining the impact of mentioned concepts.
Key Points
- LLMs are increasingly used for consequential decisions, making their explanations important for auditing model behavior.
- Explanations from LLMs can be unfaithful, not reflecting the model's actual reasoning.
- Unfaithful explanations have two dimensions: incompleteness (omitting influential factors) and unsoundness (citing non-influential factors).
- Existing faithfulness approaches include training-time methods (requiring model weights and extensive computation) and test-time methods (largely focused on unsoundness).
- The new approach is a test-time method that directly targets incompleteness.
- The method involves removing concepts not credited in the model's explanation from the input.
- The model is then re-queried on this reduced input to preserve the influence of mentioned concepts.
Context
According to the authors, LLMs often provide both an answer and an explanation in response to a question. The faithfulness of these explanations is critical for auditing model behavior, especially as LLMs are deployed in contexts involving significant decisions. The paper distinguishes between two types of unfaithful explanations: incompleteness, where the explanation omits factors that genuinely influenced the answer, and unsoundness, where the explanation cites factors that did not influence the model's decision. While some existing test-time methods address unsoundness, the presented approach specifically targets incompleteness.
Why It Matters
Improving the faithfulness of LLM explanations is crucial for builders and researchers who rely on these models for auditing and understanding their decision-making processes. Addressing incompleteness can lead to more transparent and trustworthy AI systems, which is vital for deployment in sensitive applications.
What To Do
- Review the paper's methodology for removing uncredited concepts from LLM inputs.
- Consider how this test-time approach could be integrated into existing LLM auditing pipelines.
- Evaluate the trade-offs between this method and training-time approaches for improving explanation faithfulness.
- Note the distinction between incompleteness and unsoundness when designing or evaluating LLM explanation systems.