Stance Detection with Collaborative Role-Infused LLM-Based Agents
- URL: http://arxiv.org/abs/2310.10467v2
- Date: Tue, 16 Apr 2024 06:06:43 GMT
- Title: Stance Detection with Collaborative Role-Infused LLM-Based Agents
- Authors: Xiaochong Lan, Chen Gao, Depeng Jin, Yong Li,
- Abstract summary: Stance detection is vital for content analysis in web and social media research.
However, stance detection requires advanced reasoning to infer authors' implicit viewpoints.
We design a three-stage framework in which LLMs are designated distinct roles.
We achieve state-of-the-art performance across multiple datasets.
- Score: 39.75103353173015
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Stance detection automatically detects the stance in a text towards a target, vital for content analysis in web and social media research. Despite their promising capabilities, LLMs encounter challenges when directly applied to stance detection. First, stance detection demands multi-aspect knowledge, from deciphering event-related terminologies to understanding the expression styles in social media platforms. Second, stance detection requires advanced reasoning to infer authors' implicit viewpoints, as stance are often subtly embedded rather than overtly stated in the text. To address these challenges, we design a three-stage framework COLA (short for Collaborative rOle-infused LLM-based Agents) in which LLMs are designated distinct roles, creating a collaborative system where each role contributes uniquely. Initially, in the multidimensional text analysis stage, we configure the LLMs to act as a linguistic expert, a domain specialist, and a social media veteran to get a multifaceted analysis of texts, thus overcoming the first challenge. Next, in the reasoning-enhanced debating stage, for each potential stance, we designate a specific LLM-based agent to advocate for it, guiding the LLM to detect logical connections between text features and stance, tackling the second challenge. Finally, in the stance conclusion stage, a final decision maker agent consolidates prior insights to determine the stance. Our approach avoids extra annotated data and model training and is highly usable. We achieve state-of-the-art performance across multiple datasets. Ablation studies validate the effectiveness of each design role in handling stance detection. Further experiments have demonstrated the explainability and the versatility of our approach. Our approach excels in usability, accuracy, effectiveness, explainability and versatility, highlighting its value.
Related papers
- Mixture of In-Context Experts Enhance LLMs' Long Context Awareness [51.65245442281049]
Large language models (LLMs) exhibit uneven awareness of different contextual positions.
We introduce a novel method called Mixture of In-Context Experts'' (MoICE) to address this challenge.
MoICE comprises two key components: a router integrated into each attention head within LLMs and a lightweight router-only training optimization strategy.
arXiv Detail & Related papers (2024-06-28T01:46:41Z) - Meta Reasoning for Large Language Models [58.87183757029041]
We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs)
MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task.
We evaluate the effectiveness of MRP through comprehensive benchmarks.
arXiv Detail & Related papers (2024-06-17T16:14:11Z) - TM-TREK at SemEval-2024 Task 8: Towards LLM-Based Automatic Boundary Detection for Human-Machine Mixed Text [0.0]
This paper explores the ability of large language models to identify boundaries in human-written and machine-generated mixed texts.
Our ensemble model of LLMs achieved first place in the 'Human-Machine Mixed Text Detection' sub-task of the SemEval'24 Competition Task 8.
arXiv Detail & Related papers (2024-04-01T03:54:42Z) - Can Large Language Models Identify Authorship? [18.378744138365537]
Large Language Models (LLMs) have demonstrated exceptional capacity for reasoning and problem-solving.
This paper conducts a comprehensive evaluation of LLMs in authorship analysis.
arXiv Detail & Related papers (2024-03-13T03:22:02Z) - Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent
Detection [34.135738700682055]
This paper conducts a comprehensive evaluation of large language models (LLMs) represented by ChatGPT.
We find that LLMs exhibit strong zero-shot and few-shot capabilities, but is still at a disadvantage compared to models fine-tuned with full resource.
arXiv Detail & Related papers (2024-02-27T07:02:10Z) - Bridging Causal Discovery and Large Language Models: A Comprehensive
Survey of Integrative Approaches and Future Directions [10.226735765284852]
Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields of study with significant implications for artificial intelligence.
This paper presents a comprehensive survey of the integration of LLMs, such as GPT4, into CD tasks.
arXiv Detail & Related papers (2024-02-16T20:48:53Z) - Improving Open Information Extraction with Large Language Models: A
Study on Demonstration Uncertainty [52.72790059506241]
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text.
Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
arXiv Detail & Related papers (2023-09-07T01:35:24Z) - LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset,
Framework, and Benchmark [81.42376626294812]
We present Language-Assisted Multi-Modal instruction tuning dataset, framework, and benchmark.
Our aim is to establish LAMM as a growing ecosystem for training and evaluating MLLMs.
We present a comprehensive dataset and benchmark, which cover a wide range of vision tasks for 2D and 3D vision.
arXiv Detail & Related papers (2023-06-11T14:01:17Z) - Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate [85.89346248535922]
We propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution.
Our framework encourages divergent thinking in LLMs which would be helpful for tasks that require deep levels of contemplation.
arXiv Detail & Related papers (2023-05-30T15:25:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.