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Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis

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arxiv 2301.13819 v2 pith:SMFJSFA2 submitted 2023-01-24 cs.CL cs.LG

Ruibo Tu , Chao Ma , Cheng Zhang This is my paper · ORCID
classification cs.CLcs.LG
keywords answercausalchatgptdiscoverylanguagequestionsabilitybenchmark
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ChatGPT has demonstrated exceptional proficiency in natural language conversation, e.g., it can answer a wide range of questions while no previous large language models can. Thus, we would like to push its limit and explore its ability to answer causal discovery questions by using a medical benchmark (Tu et al. 2019) in causal discovery.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 16 citations worldwide. Full citation record

  1. Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery

    cs.AI 2025-06 conditional novelty 5.0 of 10

    A learning-to-rank model chooses informative knowledge-graph paths between entity pairs, and adding the top path to zero-shot prompts improves LLM causal classification by up to 44.4 F1 points.

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