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Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations

Summary

The paper asks whether ChatGPT grasps relations that hold between sentences, which require more than surface-level reading. The authors examine three relation families: temporal, causal and discourse. They run the model (gpt-3.5-turbo-0301, queried in April 2023) on the full test sets of 11 benchmarks. These cover temporal ordering, causal reasoning, PDTB2.0-style explicit and implicit discourse relations, and multi-party dialogue discourse parsing.

Each task is cast as a multiple-choice question and tested with three prompt types: a plain zero-shot prompt, an expert-designed prompt (PE), and in-context learning (ICL) with one hand-picked training example per class. Results are compared with random guessing, fine-tuned models and the published state of the art.

ChatGPT handles causality well. It does poorly on event ordering, on implicit discourse relations that lack a connective, and especially on recovering dialogue structure. The study gives the first systematic baselines for these sentence-pair tasks and shows where LLM understanding still falls short.

Key points

  • Temporal relation extraction (Micro-F1) is the weakest area. The best ChatGPT scores are 27.0 on TB-Dense, 47.9 on MATRES and 16.8 on TDDMan. Fine-tuned SOTA models reach 68.7, 84.0 and 45.5 on the same benchmarks. On TDDMan, every ChatGPT variant scores below random guessing (17.3).
  • Causal reasoning is a strength, with the best results on COPA. ChatGPT beats a fine-tuned RoBERTa on two of the three causal benchmarks.
  • Explicit discourse relations are mostly recognized correctly, since the connectives give strong cues. Implicit discourse relation recognition remains hard.
  • On dialogue discourse parsing (STAC, Molweni), ChatGPT falls far behind supervised parsers, by as much as 50% F1 on link prediction. ICL examples help joint link-and-relation prediction but can hurt link prediction alone.
  • Prompts and code are released at the HKUST-KnowComp GitHub repository "ChatGPT-Inter-Sentential-Relations". NLI and entailment tasks are excluded because earlier studies already covered them.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Discourse analysis

Cited by

84 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 76 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.findings-eacl.47 · arXiv 2304.14827

@inproceedings{chan2024exploring,
  title = {{Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations}},
  author = {Chan, Chunkit and Jiayang, Cheng and Wang, Weiqi and Jiang, Yuxin and Fang, Tianqing and Liu, Xin and Song, Yangqiu},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2024},
  year = {2024},
  doi = {10.18653/v1/2024.findings-eacl.47},
  eprint = {2304.14827},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2024.findings-eacl.47}
}

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