Alignment for Honesty (2312.07000v2)

Abstract: Recent research has made significant strides in aligning LLMs with helpfulness and harmlessness. In this paper, we argue for the importance of alignment for \emph{honesty}, ensuring that LLMs proactively refuse to answer questions when they lack knowledge, while still not being overly conservative. However, a pivotal aspect of alignment for honesty involves discerning an LLM's knowledge boundaries, which demands comprehensive solutions in terms of metric development, benchmark creation, and training methodologies. We address these challenges by first establishing a precise problem definition and defining ``honesty'' inspired by the Analects of Confucius. This serves as a cornerstone for developing metrics that effectively measure an LLM's honesty by quantifying its progress post-alignment. Furthermore, we introduce a flexible training framework which is further instantiated by several efficient fine-tuning techniques that emphasize honesty without sacrificing performance on other tasks. Our extensive experiments reveal that these aligned models show a marked increase in honesty, as indicated by our proposed metrics. We open-source all relevant resources to facilitate future research at \url{https://github.com/GAIR-NLP/alignment-for-honesty}.

• The paper introduces a novel framework to evaluate AI honesty by measuring over-conservativeness and prudence in responses.
• It applies diverse training approaches, including supervised fine-tuning and training-free techniques, to optimize models’ admission of knowledge limits.
• Empirical results confirm improved alignment with human values while highlighting opportunities for refining knowledge boundary definitions.

Introduction

The concept of alignment in LLMs is a critical area of research geared at ensuring that these models are consistently resonant with human values, predicated on principles that encapsulate helpfulness, harmlessness, and honesty. While substantial progress has been made in fostering helpfulness and harmless attributes, the aspect of honesty remains relatively less explored. Honesty in AI, as contended in this paper, explores a model's ability to either provide correct answers based on its knowledge or proactively admit lack of knowledge by refusing to answer – an intricate challenge due to its dependency on accurately discerning a model's knowledge limits. This paper addresses these challenges by offering a systematic framework anchored in the classic adage from Confucius advocating for forthrightness in admitting one’s knowledge or ignorance.

Evaluation and Framework

Presenting a methodology well-suited for evaluating the evolvement of model honesty pre- and post-alignment, the research proposes metrics that capture a model's increased propensity to abstain from responding outside its knowledge field. Two key metrics introduced are: the 'over-conservativeness score' tracking unwarranted cautiousness in response, and the 'prudence score' evaluating the model’s capacity to appropriately withhold an answer when in doubt. These are combined to form the holistic 'honesty score' that assesses the post-alignment honesty of the LLM.

Methodology and Experiments

The paper proposes various training methodologies designed to augment model honesty without detrimentally impacting other performance aspects. Methods such as training-free (using prompts), supervised fine-tuning, and differentiating strategies based on expected model accuracy offer a spectrum of approaches to optimize for honesty. Empirical evidence across an array of tests demonstrates the efficacy of these methods, particularly showing that models indeed become better aligned with the principle of honesty when these methods are applied.

Discussion and Future Work

Moreover, the paper identifies limitations and avenues for future exploration, such as refining methods to define knowledge boundaries within models and expanding the definition of honesty to cover longer-form generation and retrieval scenarios. It underlines the need for a nuanced understanding of these concepts and presents a glossary to help navigate the complex terrain of AI alignment. Looking forward, this piece of work sets the stage for continued innovation within the field of constructing AI that is both reliable and aligned with human intentions.