CMU researchers propose QueRE: an AI approach to extracting useful features from an LLM

Large language models (LLMs) have become an integral part of various artificial intelligence applications, demonstrating capabilities in natural language processing, decision making, and creative tasks. However, critical challenges remain in understanding and predicting their behaviors. Treating LLMs as black boxes complicates efforts to assess their reliability, particularly in contexts where errors can have significant consequences. Traditional approaches often rely on internal model states or gradients to interpret behaviors, which are not available for API-based closed source models. This limitation raises an important question: how can we effectively evaluate LLM behavior with only black box access? The problem is further exacerbated by adverse influences and potential misrepresentation of models through APIs, highlighting the need for robust and generalizable solutions.

To address these challenges, researchers at Carnegie Mellon University have developed wants (Question representation elicitation). This method is designed for black-box LLM and extracts low-dimensional, task-independent representations by querying models with trace cues about their results. These representations, based on probabilities associated with the elicited responses, are used to train predictors of model performance. In particular, QueRE performs comparable or even better than some white-box techniques in terms of reliability and generalization.

Unlike methods that rely on internal model states or complete outcome distributions, QueRE relies on accessible outcomes, such as top-k probabilities available through most APIs. When such probabilities are not available, they can be approximated by sampling. QueRE's features also enable evaluations such as detecting adversarially influenced models and distinguishing between architectures and sizes, making it a versatile tool for understanding and using LLM.

Technical details and benefits of QueRE

QueRE operates by constructing feature vectors derived from elicitation questions posed to the LLM. For a given input and model response, these questions evaluate things like confidence and correctness. Questions like “Do you trust your answer?” or “Can you explain your answer?” Allow the extraction of probabilities that reflect the model's reasoning.

The extracted features are then used to train linear predictors for various tasks:

1. Performance prediction: Evaluate whether the output of a model is correct at the instance level.
2. Adversary Detection: Identify when responses are influenced by malicious messages.
3. Model differentiation: Distinguish between different architectures or configurations, such as identifying smaller models misrepresented as larger.

By relying on low-dimensional representations, QueRE supports strong generalization across tasks. Its simplicity ensures scalability and reduces the risk of overfitting, making it a practical tool for auditing and implementing LLM in various applications.

Results and insights

Experimental evaluations demonstrate the effectiveness of QueRE in several dimensions. When predicting LLM performance on question answering (QA) tasks, QueRE consistently outperformed baselines based on internal states. For example, in open quality control benchmarks such as SQuAD and Natural Questions (NQ), QueRE achieved an area under the receiver operating characteristic curve (AUROC) greater than 0.95. Similarly, it excelled in detecting adversarially influenced models, outperforming other black-box methods.

QueRE also proved to be robust and transferable. Its features were successfully applied to out-of-distribution tasks and different LLM configurations, validating its adaptability. Low-dimensional representations facilitated efficient training of simple models, ensuring computational feasibility and strong generalization limits.

Another notable result was QueRE's ability to use random natural language sequences as fetch cues. These sequences often matched or exceeded the performance of structured queries, highlighting the flexibility of the method and the potential for diverse applications without extensive manual engineering.

Conclusion

QueRE offers a practical and effective approach to understanding and optimizing black-box LLMs. By transforming elicitation responses into actionable features, QueRE provides a scalable and robust framework for predicting model behavior, detecting adverse influences, and differentiating architectures. Its success in empirical evaluations suggests that it is a valuable tool for researchers and practitioners seeking to improve the reliability and security of LLMs.

As ai systems evolve, methods like QueRE will play a crucial role in ensuring transparency and reliability. Future work could explore the possibility of expanding the applicability of QueRE to other modalities or refining its elicitation strategies to improve performance. For now, QueRE represents a thoughtful response to the challenges posed by modern ai systems.

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