PromptExp: Multi-granularity Prompt Explanation of Large Language Models

• URL: http://arxiv.org/abs/2410.13073v3
• Date: Wed, 30 Oct 2024 14:15:49 GMT
• Title: PromptExp: Multi-granularity Prompt Explanation of Large Language Models
• Authors: Ximing Dong, Shaowei Wang, Dayi Lin, Gopi Krishnan Rajbahadur, Boquan Zhou, Shichao Liu, Ahmed E. Hassan,
• Abstract summary: We introduce PromptExp, a framework for multi-granularity prompt explanations by aggregating token-level insights. PromptExp supports both white-box and black-box explanations and extends explanations to higher granularity levels. We evaluate PromptExp in case studies such as sentiment analysis, showing the perturbation-based approach performs best.
• Score: 16.259208045898415
• License:
• Abstract: Large Language Models excel in tasks like natural language understanding and text generation. Prompt engineering plays a critical role in leveraging LLM effectively. However, LLMs black-box nature hinders its interpretability and effective prompting engineering. A wide range of model explanation approaches have been developed for deep learning models, However, these local explanations are designed for single-output tasks like classification and regression,and cannot be directly applied to LLMs, which generate sequences of tokens. Recent efforts in LLM explanation focus on natural language explanations, but they are prone to hallucinations and inaccuracies. To address this, we introduce PromptExp , a framework for multi-granularity prompt explanations by aggregating token-level insights. PromptExp introduces two token-level explanation approaches: 1. an aggregation-based approach combining local explanation techniques, and 2. a perturbation-based approach with novel techniques to evaluate token masking impact. PromptExp supports both white-box and black-box explanations and extends explanations to higher granularity levels, enabling flexible analysis. We evaluate PromptExp in case studies such as sentiment analysis, showing the perturbation-based approach performs best using semantic similarity to assess perturbation impact. Furthermore, we conducted a user study to confirm PromptExp's accuracy and practical value, and demonstrate its potential to enhance LLM interpretability.

Related papers

• 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)
• 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)
• Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models [54.21695754082441]
  We propose a framework to teach Large Language Models (LLMs) to generate explainable stock predictions. A reflective agent learns how to explain past stock movements through self-reasoning, while the PPO trainer trains the model to generate the most likely explanations. Our framework can outperform both traditional deep-learning and LLM methods in prediction accuracy and Matthews correlation coefficient.
  arXiv  Detail & Related papers  (2024-02-06T03:18:58Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations [26.340786701393768]
  Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding. Current solutions for dialogue-based explanations, however, often require external tools and modules and are not easily transferable to tasks they were not designed for. We present an easily accessible tool that allows users to chat with any state-of-the-art large language model (LLM) about its behavior.
  arXiv  Detail & Related papers  (2024-01-23T09:11:07Z)
• Sparsity-Guided Holistic Explanation for LLMs with Interpretable Inference-Time Intervention [53.896974148579346]
  Large Language Models (LLMs) have achieved unprecedented breakthroughs in various natural language processing domains. The enigmatic black-box'' nature of LLMs remains a significant challenge for interpretability, hampering transparent and accountable applications. We propose a novel methodology anchored in sparsity-guided techniques, aiming to provide a holistic interpretation of LLMs.
  arXiv  Detail & Related papers  (2023-12-22T19:55:58Z)
• Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning [50.00090601424348]
  Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks. We propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs.
  arXiv  Detail & Related papers  (2023-11-13T06:13:38Z)
• Towards LLM-guided Causal Explainability for Black-box Text Classifiers [16.36602400590088]
  We aim to leverage the instruction-following and textual understanding capabilities of recent Large Language Models to facilitate causal explainability. We propose a three-step pipeline via which, we use an off-the-shelf LLM to identify the latent or unobserved features in the input text. We experiment with our pipeline on multiple NLP text classification datasets, and present interesting and promising findings.
  arXiv  Detail & Related papers  (2023-09-23T11:22:28Z)
• Complementary Explanations for Effective In-Context Learning [77.83124315634386]
  Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts. This work aims to better understand the mechanisms by which explanations are used for in-context learning.
  arXiv  Detail & Related papers  (2022-11-25T04:40:47Z)

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.