Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness (2308.16175v2)

Abstract: We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained LLM by estimating a numeric confidence score for any output it generated. Our uncertainty quantification technique works for any LLM accessible only via a black-box API, whose training data remains unknown. By expending a bit of extra computation, users of any LLM API can now get the same response as they would ordinarily, as well as a confidence estimate that cautions when not to trust this response. Experiments on both closed and open-form Question-Answer benchmarks reveal that BSDetector more accurately identifies incorrect LLM responses than alternative uncertainty estimation procedures (for both GPT-3 and ChatGPT). By sampling multiple responses from the LLM and considering the one with the highest confidence score, we can additionally obtain more accurate responses from the same LLM, without any extra training steps. In applications involving automated evaluation with LLMs, accounting for our confidence scores leads to more reliable evaluation in both human-in-the-loop and fully-automated settings (across both GPT 3.5 and 4).

• The paper presents BETECTOR, a method that quantifies LLM answer reliability using dual evaluations of observed consistency and self-reflection.
• BETECTOR outperforms alternative uncertainty techniques on benchmarks like GSM8K and TriviaQA, showing significant improvements in answer accuracy.
• The method enables safer LLM deployment in high-stakes environments by providing actionable confidence scores for informed decision-making.

Enhancing LLM Response Trustworthiness through BETECTOR

Overview

LLMs have become a cornerstone of modern AI applications, driving advancements in natural language understanding and generation. However, their reliability in high-value applications is circumscribed by the challenge of hallucinated or overconfident responses. Addressing this critical issue, the method introduced in the paper, BETECTOR, seeks to quantifiably enhance the trustworthiness of answers provided by any LLM, irrespective of access to its internal workings or training data. This method represents a significant step forward in mitigating the risks associated with deploying LLMs in sensitive or high-stakes environments.

BETECTOR Methodology

The BETECTOR method operates by generating a confidence score alongside the conventional LLM output, offering an assessment of the response's reliability. This process involves two core evaluations: Observed Consistency and Self-reflection Certainty. Observed Consistency is quantified through the generation and evaluation of multiple responses from an LLM to the same query, aiming to detect contradictions or variances that suggest uncertainty. Self-reflection Certainty, alternatively, leverages the LLM's capability to introspectively assess the reliability of its generated output. The combination of these approaches into an overall confidence estimate presents a nuanced, dual-dimensional perspective on answer trustworthiness.

Experimental Validation

The efficacy of BETECTOR was rigorously validated across various domains, including reasoning, arithmetic, and fact-based knowledge using benchmark datasets (GSM8K, SVAMP, CSQA, and TriviaQA) and LLM implementations (GPT-3, GPT-3.5, and ChatGPT). Compared to alternative uncertainty estimation techniques, BETECTOR consistently delivered superior performance, evidenced by significant improvements in the accuracy of LLM responses. Specifically, it showed remarkable proficiency in enhancing the reliability of answers from LLMs, with confidence scores demonstrating pronounced alignment with the factual correctness across numerous evaluation metrics.

Practical Applications

Beyond its theoretical contributions, BETECTOR's utility is underscored in its practical applications. By enabling the identification of less reliable LLM outputs, it facilitates informed decision-making regarding the utilization or disregard of specific responses. This capability is particularly advantageous in applications where the stakes of incorrect answers are high, providing a mechanism to adaptively engage human oversight or seek alternative sources of information when confidence levels are low.

Future Perspectives

While BETECTOR marks a substantial advance in the operational utility of LLMs, it prompts further inquiry into the optimization of confidence estimation methodologies. Future work may explore adaptive strategies that balance the computational costs of enhanced confidence evaluation with the necessity for precision in high-risk contexts. Moreover, the generalizability of BETECTOR's approach invites exploration into its applicability across a broader spectrum of AI models and tasks, potentially extending its benefits to the wider landscape of machine learning applications.

In summary, BETECTOR offers a powerful tool for augmenting the reliability and safe deployment of LLMs, contributing to the ongoing evolution of generative AI towards more trustworthy and versatile implementations.