忠实性与合理性：论大型语言模型解释的（不）可靠性
Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models
AI总结：
本文探讨LLM自我解释中忠实性与合理性的矛盾，指出追求合理性可能损害忠实性，并呼吁开发新方法提升高风险场景下解释的可靠性。
摘要：
大型语言模型（LLMs）被部署为多种自然语言处理（NLP）应用的强大工具。近期研究表明，现代LLMs能够生成自我解释（SEs），这些解释引出其中间推理步骤以说明其行为。自我解释因其对话性和看似合理的特点而得到广泛采用。然而，对其忠实性几乎没有了解。在本工作中，我们探讨了LLMs生成的自我解释中忠实性与合理性之间的二分法。我们认为，虽然LLMs擅长生成看似合理且对人类用户显得逻辑连贯的解释，但这些解释并不一定与LLMs的推理过程一致，从而引发对其忠实性的担忧。我们强调，当前提高解释合理性的趋势（主要由对用户友好界面的需求驱动）可能以牺牲其忠实性为代价。我们断言，在用于高风险决策的LLMs中，解释的忠实性至关重要。此外，我们强调需要系统性地刻画不同实际应用场景对忠实性和合理性的要求，并确保解释满足这些需求。尽管有多种方法可以提高合理性，但提高忠实性仍是一个开放挑战。我们呼吁社区开发新方法以增强自我解释的忠实性，从而使LLMs能够在各种高风险场景中透明部署。
英文摘要：
Large Language Models (LLMs) are deployed as powerful tools for several natural language processing (NLP) applications. Recent works show that modern LLMs can generate self-explanations (SEs), which elicit their intermediate reasoning steps for explaining their behavior. Self-explanations have seen widespread adoption owing to their conversational and plausible nature. However, there is little to no understanding of their faithfulness. In this work, we discuss the dichotomy between faithfulness and plausibility in SEs generated by LLMs. We argue that while LLMs are adept at generating plausible explanations -- seemingly logical and coherent to human users -- these explanations do not necessarily align with the reasoning processes of the LLMs, raising concerns about their faithfulness. We highlight that the current trend towards increasing the plausibility of explanations, primarily driven by the demand for user-friendly interfaces, may come at the cost of diminishing their faithfulness. We assert that the faithfulness of explanations is critical in LLMs employed for high-stakes decision-making. Moreover, we emphasize the need for a systematic characterization of faithfulness-plausibility requirements of different real-world applications and ensure explanations meet those needs. While there are several approaches to improving plausibility, improving faithfulness is an open challenge. We call upon the community to develop novel methods to enhance the faithfulness of self explanations thereby enabling transparent deployment of LLMs in diverse high-stakes settings.
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