Zero-Shot Stance Detection on Social Media with Explicit Reasoning

Concetti Chiave

Stance Reasoner enhances zero-shot stance detection by leveraging explicit reasoning over background knowledge, outperforming current models.

Sintesi

Abstract: Stance detection on social media is crucial for extracting opinions. Zero-shot stance detection is challenging but essential for new targets.
Introduction: Stance Reasoner focuses on zero-shot stance detection using explicit reasoning and world knowledge to predict stances accurately.
Methodology: Stance Reasoner utilizes CoT for reasoning, optimized prompts, and self-consistency to enhance model accuracy.
Experiments: Evaluation on Twitter datasets shows Stance Reasoner outperforms state-of-the-art models in generalization and interpretability.
Results: Stance Reasoner achieves superior performance across various targets, demonstrating its effectiveness in zero-shot stance detection.
Qualitative Analysis: Examples showcase the model's ability to identify annotation errors, handle ambiguous contexts, and detect rhetorical devices.
Prior Work: Comparison with existing methods highlights the unique approach of Stance Reasoner in enhancing stance detection tasks.

Statistiche

"Stance Reasoner outperforms all baseline methods including fully supervised models."
"Our method achieved the best average F1 score of 72.6."
"The larger LLaMA 65B model consistently achieves better performance."

Citazioni

"Stance Reasoner generates explicit reasoning over background knowledge to predict the stance of a given tweet regarding a target."
"Our empirical results show that Stance Reasoner outperforms the current state-of-the-art models on a Twitter dataset."

Approfondimenti chiave tratti da

Stance Reasoner

by Maksym Taran... alle arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14895.pdf

Domande più approfondite

How can Stance Reasoner be adapted for longer texts beyond tweets?

Stance Reasoner can be adapted for longer texts by modifying the prompt structure and incorporating more diverse examples that cover a wider range of reasoning strategies. Since longer texts may contain more complex arguments and nuanced stances, the model's prompt should include a mix of explicit and implicit stance examples with varying rhetorical devices. Additionally, providing the model with additional context or background information related to the topic discussed in the text can help improve its understanding and inference capabilities. By adjusting the input format and expanding the scope of examples, Stance Reasoner can effectively analyze longer texts such as opinion pieces or blog posts.

What ethical considerations should be taken into account when deploying automated stance detection tools?

When deploying automated stance detection tools, several ethical considerations must be taken into account to ensure responsible use and mitigate potential harms. Some key ethical considerations include:

Bias and Fairness: Ensuring that the models are trained on diverse datasets to prevent bias against certain groups or viewpoints.
Transparency: Providing clear explanations for how decisions are made by the model to enhance transparency and accountability.
Privacy: Safeguarding user data privacy during data collection, storage, and analysis processes.
Misinformation: Addressing concerns related to spreading misinformation or amplifying polarizing content through automated tools.
User Consent: Obtaining informed consent from users before collecting their data for training or evaluation purposes.
Algorithmic Accountability: Establishing mechanisms for auditing model performance, detecting biases, and addressing any unintended consequences.

By considering these ethical principles throughout the development and deployment stages of automated stance detection tools, developers can promote fairness, transparency, privacy protection, and responsible usage.

How does the inclusion of diverse reasoning strategies impact the overall performance of the model?

The inclusion of diverse reasoning strategies significantly impacts the overall performance of a stance detection model like Stance Reasoner in several ways:

Improved Generalization - Diverse reasoning strategies expose the model to various types of arguments used in different contexts, enhancing its ability to generalize across different topics or targets not seen during training.
Enhanced Interpretability - By including a wide range of reasoning patterns in training examples, interpretable explanations are generated along with predictions which aid in understanding why specific stances were predicted by highlighting underlying logic chains.
Robustness Against Ambiguity - Exposure to multiple reasoning styles helps models navigate ambiguous contexts better where determining stances solely based on textual cues might be challenging without additional context clues.
4 .Detection Accuracy - Models trained on diverse sets of reasoning scenarios tend to perform better at identifying subtle nuances within text that indicate specific stances towards given targets.

Overall ,the incorporation 0f varied reasonings trategies enriches th emodel's learning process leadingto improved accuracy generaliztion interpretabilityand robustnessto ambiguity