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Sentiment Analysis through LLM Negotiations

Paper · 2023 · Xiaofei Sun, Xiaoya Li, Shengyu Zhang et al.

Summary

Large language models are typically applied to sentiment classification through a single in-context learning (ICL) prompt answered in one pass. A single answer like this can miss subtle cues such as irony or clauses that pull in opposite directions. The authors propose having two LLMs negotiate instead. One model, the generator, assigns a polarity label and must justify it with explicit reasoning. The other, the discriminator, assesses whether that verdict is credible and explains its own judgement. The two exchange turns, and the generator may revise its decision, until they agree. The roles can also be swapped, with a third LLM settling cases where the swapped runs reach different outcomes. With GPT-3.5 and GPT-4, the approach is tested on SST-2, Movie Review, Twitter, Yelp, Amazon and IMDB. Negotiation outperforms plain ICL on every benchmark, and the authors report beating supervised baselines on Movie Review and Twitter. The study shows that letting different models argue with rationales is a training-free way to make few-shot sentiment decisions more accurate and easier to interpret.

Key points

  • Self-negotiation. Using one LLM in both roles already improves on vanilla ICL across the six datasets, by about +0.9 accuracy on average for GPT-3.5 and +1.0 for GPT-4.
  • Two different LLMs. Pairing distinct models beats self-negotiation by +1.7 on MR, +2.1 on Twitter and +2.3 on IMDB.
  • Comparison with supervised models. The full multi-model setup exceeds RoBERTa-Large by +0.9 on MR. It reaches 94.1 on SST-2, 96.3 on Yelp-2, 87.2 on Amazon-2 and 94.5 on IMDB, which is +6.0 over ICL on IMDB.
  • Role assignment (Twitter ablation). GPT-4 should be the generator. G3.5-D4 scores 68.8, which is below standalone GPT-4 at 69.5, while G4-D3.5 even beats G4-D4. With GPT-4 generating, consensus within two turns rises to 76%, compared with 65% for G3.5-D4.
  • Role of reasoning. Removing the rationales hurts the negotiation setup most (−2.3, from 74.6 to 72.3). Single models lose less: −1.2 for GPT-3.5 and −0.9 for GPT-4.

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, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksGeneral Social Sciences · 227 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksSentiment & emotion · 972 works

Cite

Cite as Xiaofei Sun, Xiaoya Li, Shengyu Zhang, Shuhe Wang, Fei Wu, Jiwei Li, Tianwei Zhang and Guoyin Wang. 2023. Sentiment Analysis through LLM Negotiations. arXiv:2311.01876.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2311.01876 · arXiv 2311.01876

@misc{sun2023sentiment,
  title = {{Sentiment Analysis through LLM Negotiations}},
  author = {Xiaofei Sun, Xiaoya Li, Shengyu Zhang, Shuhe Wang, Fei Wu, Jiwei Li, Tianwei Zhang and Guoyin Wang},
  year = {2023},
  doi = {10.48550/arxiv.2311.01876},
  eprint = {2311.01876},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2311.01876}
}

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