Abstract
Recent research has made significant strides in applying alignment techniques to enhance the helpfulness and harmlessness of LLMs in accordance with human intentions. In this paper, we argue for the importance of alignment for 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 the limits of an LLM's knowledge, which is far from straightforward. This challenge demands comprehensive solutions in terms of metric development, benchmark creation, and training methodologies. In this paper, 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 a wealth of resources to facilitate future research at https://github.com/GAIR-NLP/alignment-for-honesty, including honesty-aligned models, training and evaluation datasets for honesty alignment, concept glossary, as well as all relevant source code.
Overview
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The paper examines how to align LLMs with human values, specifically focusing on the attribute of honesty.
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It introduces a framework for assessing model honesty based on Confucian principles and evaluates the propensity of a model to refrain from answering beyond its knowledge limits.
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New metrics such as the 'over-conservativeness score' and 'prudence score' are proposed, which together form a comprehensive 'honesty score'.
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Various training methodologies are explored, including training-free approaches, supervised fine-tuning, and differentiated strategies aimed at enhancing model honesty.
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The research includes an assessment of limitations, suggests directions for future work, and contributes to the development of reliable AI in alignment with human values.
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, explore 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 realm. 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 realm of constructing AI that is both reliable and aligned with human intentions.
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GitHub - GAIR-NLP/alignment-for-honesty (61 stars)