Explaining Patterns in Data with Language Models via Interpretable Autoprompting (2210.01848v2)

Abstract: LLMs have displayed an impressive ability to harness natural language to perform complex tasks. In this work, we explore whether we can leverage this learned ability to find and explain patterns in data. Specifically, given a pre-trained LLM and data examples, we introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data. iPrompt iteratively alternates between generating explanations with an LLM and reranking them based on their performance when used as a prompt. Experiments on a wide range of datasets, from synthetic mathematics to natural-language understanding, show that iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions. Moreover, the prompts produced by iPrompt are simultaneously human-interpretable and highly effective for generalization: on real-world sentiment classification datasets, iPrompt produces prompts that match or even improve upon human-written prompts for GPT-3. Finally, experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery. All code for using the methods and data here is made available on Github.

Interpretation of Data Patterns through Interpretable Autoprompting: A Detailed Analysis

The paper "iPrompt: Explaining Data Patterns in Natural Language via Interpretable Autoprompting" explores a novel methodology to enhance the interpretability of LLMs through automated prompt generation. This methodology, termed interpretable autoprompting, aims to leverage LLMs to generate meaningful natural language explanations that describe data patterns, thereby shifting from mere prediction tasks to generating interpretable insights from datasets.

Summary of Methodology

The paper introduces iPrompt, an iterative local search algorithm that discovers a natural language prompt capable of articulating patterns within datasets. iPrompt operates through three primary stages: proposing candidate prompts, reranking these prompts based on LLM performance, and iteratively refining them through exploration. The distinction of iPrompt from traditional autoprompting techniques lies in its emphasis on generating semantically meaningful and human-interpretable prompts that can facilitate understanding and generalization to different LLM settings.

Key Findings

Experiments were conducted across a diverse set of tasks, including synthetic math problems, natural language understanding tasks from ANLI and instruction datasets, sentiment classification, and scientific datasets such as chemical compound characteristics and fMRI datasets. The effectiveness of iPrompt was notably pronounced in several aspects:

1. Performance Outstripping Baselines: iPrompt significantly outperformed existing autoprompting methods such as AutoPrompt and zero-shot suffix decoding. It registered higher mean reciprocal rank (MRR) and correctness scores across different datasets, thereby proving its efficacy in generating accurate and interpretable dataset descriptions.
2. Generalization Across Models: The generated prompts were transferable to other LLMs, showing particularly robust performance with larger models such as GPT-3, and exceeding human-written prompts in select cases of sentiment analysis.
3. Scientific Insight Extraction: In scientific datasets, iPrompt successfully extracted relevant descriptors, such as identifying toxicity in chemical datasets or differentiating protein characteristics, indicating its potential utility in scientific discovery tasks.

Implications and Future Directions

The paper's primary contribution is demonstrating that iPrompt can bridge the gap between technical model performance and human interpretability in LLMs. This advances the usability of LLMs in real-world applications where understanding data relationships is as critical as high prediction accuracy.

Potential Applications:

• Scientific Research: iPrompt's ability to describe complex datasets points to potential applications in fields like bioinformatics or neuroscience, where interpreting data patterns is pivotal.
• Explainability in AI: By improving transparency and offering explanations for model decisions, iPrompt can play a significant role in sectors that require explainable AI solutions, such as finance and healthcare.

Speculation on Future Developments:

The paper paves the way for further research into integrating explicit knowledge into LLM prompts and exploring multi-modal applications of interpretable autoprompting techniques. Future directions may involve refining the algorithm to enhance its efficiency and exploring its application across broader domains with varying types of unstructured data.

In conclusion, "iPrompt: Explaining Data Patterns in Natural Language via Interpretable Autoprompting" marks a commendable step towards not only leveraging LLMs for prediction but also for deriving human-interpretable insights from complex datasets, thereby extending the utility of AI systems in both academic and practical realms. The introduction of an interpretable autoprompting approach could catalyze advancements in AI transparency and cross-disciplinary research, influencing future innovations in AI-driven data analysis.