Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models

• URL: http://arxiv.org/abs/2402.04614v3
• Date: Thu, 14 Mar 2024 03:48:08 GMT
• Title: Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models
• Authors: Chirag Agarwal, Sree Harsha Tanneru, Himabindu Lakkaraju,
• Abstract summary: 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. We discuss the dichotomy between faithfulness and plausibility in SEs generated by LLMs.
• Score: 26.11408084129897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models [55.332004960574004]
  Large language models (LLMs) are widely used in decision-making, but their reliability, especially in critical tasks like healthcare, is not well-established. This paper investigates how the uncertainty of responses generated by LLMs relates to the information provided in the input prompt. We propose a prompt-response concept model that explains how LLMs generate responses and helps understand the relationship between prompts and response uncertainty.
  arXiv  Detail & Related papers  (2024-07-20T11:19:58Z)
• Towards Logically Consistent Language Models via Probabilistic Reasoning [14.317886666902822]
  Large language models (LLMs) are a promising venue for natural language understanding and generation tasks. LLMs are prone to generate non-factual information and to contradict themselves when prompted to reason about beliefs of the world. We introduce a training objective that teaches a LLM to be consistent with external knowledge in the form of a set of facts and rules.
  arXiv  Detail & Related papers  (2024-04-19T12:23:57Z)
• Towards Uncovering How Large Language Model Works: An Explainability Perspective [38.07611356855978]
  Large language models (LLMs) have led to breakthroughs in language tasks, yet the internal mechanisms that enable their remarkable generalization and reasoning abilities remain opaque. This paper aims to uncover the mechanisms underlying LLM functionality through the lens of explainability.
  arXiv  Detail & Related papers  (2024-02-16T13:46:06Z)
• Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate [75.10515686215177]
  Large Language Models (LLMs) excel in text generation, but their capability for producing faithful explanations in fact-checking remains underexamined. We propose the Multi-Agent Debate Refinement (MADR) framework, leveraging multiple LLMs as agents with diverse roles. MADR ensures that the final explanation undergoes rigorous validation, significantly reducing the likelihood of unfaithful elements and aligning closely with the provided evidence.
  arXiv  Detail & Related papers  (2024-02-12T04:32:33Z)
• FaithLM: Towards Faithful Explanations for Large Language Models [67.29893340289779]
  Large Language Models (LLMs) have become proficient in addressing complex tasks by leveraging their internal knowledge and reasoning capabilities. The black-box nature of these models complicates the task of explaining their decision-making processes. We introduce FaithLM to explain the decision of LLMs with natural language (NL) explanations.
  arXiv  Detail & Related papers  (2024-02-07T09:09:14Z)
• From Understanding to Utilization: A Survey on Explainability for Large Language Models [27.295767173801426]
  This survey underscores the imperative for increased explainability in Large Language Models (LLMs) Our focus is primarily on pre-trained Transformer-based LLMs, which pose distinctive interpretability challenges due to their scale and complexity. When considering the utilization of explainability, we explore several compelling methods that concentrate on model editing, control generation, and model enhancement.
  arXiv  Detail & Related papers  (2024-01-23T16:09:53Z)
• Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
  Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning. We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
  arXiv  Detail & Related papers  (2023-10-02T01:00:50Z)
• Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
  We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
  arXiv  Detail & Related papers  (2023-09-20T09:23:46Z)
• Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners [75.85554779782048]
  Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned textitsemantics of language tokens do the most heavy lifting during the reasoning process.
  arXiv  Detail & Related papers  (2023-05-24T07:33:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.