---
title: "Stance Detection with Collaborative Role-Infused LLM-Based"
description: "The authors propose COLA, a training-free pipeline of role-playing LLM agents with GPT-3.5 Turbo… 49 references, cited by 33 papers here."
authors: ["Xiaochong Lan", "Chen Gao", "Depeng Jin", "Yong Li"]
year: 2023
venue: "arXiv:2310.10467"
source: "https://arxiv.org/abs/2310.10467"
doi: "10.48550/arxiv.2310.10467"
arxiv: "2310.10467"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lan-2023-stance-detection-collaborative-role-infused/"
---
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# Stance Detection with Collaborative Role-Infused LLM-Based Agents

**Authors:** [Xiaochong Lan](https://md.research.elamur.ai/free/en/authors/xiaochong-lan/), [Chen Gao](https://md.research.elamur.ai/free/en/authors/chen-gao/), [Depeng Jin](https://md.research.elamur.ai/free/en/authors/depeng-jin/) and [Yong Li](https://md.research.elamur.ai/free/en/authors/yong-li/) · **Year:** 2023 · **Venue:** [arXiv:2310.10467](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-international-aaai-conference-on-web-and-social-media/) ([Proceedings of the International AAAI Conference on Web and Social Media 2023](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-international-aaai-conference-on-web-and-social-media/2023/)) · **Publisher:** [Association for the Advancement of Artificial Intelligence](https://md.research.elamur.ai/free/en/publishers/association-for-the-advancement-of-artificial-intelligence/) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2310.10467) · [PDF](https://arxiv.org/pdf/2310.10467) · [DOI 10.48550/arxiv.2310.10467](https://doi.org/10.48550/arxiv.2310.10467) · [arXiv 2310.10467](https://arxiv.org/abs/2310.10467) · [OpenAlex](https://openalex.org/W4399205331)

**Citations:** 66 citations (OpenAlex, 2026-10-14) · [33](https://md.research.elamur.ai/free/en/papers/lan-2023-stance-detection-collaborative-role-infused/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Stance detection identifies whether a text's author favors, opposes, or is neutral toward a given target. Most existing methods, including cross-target and zero-shot ones, still need labeled training data. Simple prompting of LLMs often falls short of strong non-LLM baselines for two reasons: understanding the text requires knowledge from several areas, and stances are usually implied rather than stated.

The authors propose COLA, a training-free pipeline of role-playing LLM agents with GPT-3.5 Turbo as the main backbone. It runs in three stages:
1. **Analysis.** Three agents examine the text separately, each from its own angle: one specializing in language, one in subject-matter knowledge, and one seasoned in social media.
2. **Debate.** One agent per stance label builds an argument from these analyses.
3. **Decision.** A final judge weighs the debate together with the original text.

COLA outperforms all other zero-shot methods on SEM16, P-Stance and VAST. It roughly matches in-target supervised systems, and ablations show every module helps. The framework also produces readable explanations and transfers to other classification tasks. This matters for social-media analysis where annotated data for new targets is scarce.

## Key points
- On SEM16, COLA beats the best non-LLM zero-shot stance baseline by 19.2 absolute points.
- It needs no annotated data or fine-tuning and works through API calls. Gains were also reported with open-source LLMs.
- The analyst–debater–summarizer design was reused for aspect-based sentiment analysis. It was also applied to persuasion prediction on the Wang et al. (2019) dataset, where it beat Hybrid RCNN and plain GPT-3.5 Turbo on Accuracy and Macro-F1.
- A noted limitation is weaker handling of current events. The authors plan to add a real-time knowledge base to the analysis stage.
- The paper was published at AAAI 2024. SEM16 and P-Stance tweets were obtained via the Twitter API using Tweet IDs.

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

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## Cite

Cite as: Xiaochong Lan, Chen Gao, Depeng Jin and Yong Li. 2023. Stance Detection with Collaborative Role-Infused LLM-Based Agents. arXiv:2310.10467.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{lan2023stance,
  title = {{Stance Detection with Collaborative Role-Infused LLM-Based Agents}},
  author = {Xiaochong Lan, Chen Gao, Depeng Jin and Yong Li},
  year = {2023},
  doi = {10.48550/arxiv.2310.10467},
  eprint = {2310.10467},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2310.10467}
}
```

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*Updated 3 November 2026*