Can ChatGPT Make Explanatory Inferences? Benchmarks for Abductive Reasoning

Paul Thagard
4/29/2024
cs.AI

No confident code implementation found for this paper.

Abstract

Explanatory inference is the creation and evaluation of hypotheses that provide explanations, and is sometimes known as abduction or abductive inference. Generative AI is a new set of artificial intelligence models based on novel algorithms for generating text, images, and sounds. This paper proposes a set of benchmarks for assessing the ability of AI programs to perform explanatory inference, and uses them to determine the extent to which ChatGPT, a leading generative AI model, is capable of making explanatory inferences. Tests on the benchmarks reveal that ChatGPT performs creative and evaluative inferences in many domains, although it is limited to verbal and visual modalities. Claims that ChatGPT and similar models are incapable of explanation, understanding, causal reasoning, meaning, and creativity are rebutted.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{thagard2024chatgpt,
  title  = {Can ChatGPT Make Explanatory Inferences? Benchmarks for Abductive Reasoning},
  author = {Paul Thagard},
  year   = {2024},
  eprint = {2404.18982},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.18982v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2404.18982.svg)](https://codeofpaper.com/paper/2404.18982)

Discussion