# Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification

arXiv: https://arxiv.org/abs/2309.13734 · HTML: https://mlsanity.com/paper/2309.13734/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 81 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 30 of 35)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Fast Transformer Decoding: One Write-Head is All You Need](https://mlsanity.com/paper/1911.02150/) (2019)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Textbooks Are All You Need](https://mlsanity.com/paper/2306.11644/) (2023)
- [Larger language models do in-context learning differently](https://mlsanity.com/paper/2303.03846/) (2023)
- [UL2: Unifying Language Learning Paradigms](https://mlsanity.com/paper/2205.05131/) (2022)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Reasoning Implicit Sentiment with Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.11255/) (2023)
- [How would Stance Detection Techniques Evolve after the Launch of ChatGPT?](https://mlsanity.com/paper/2212.14548/) (2022)
- [Can we trust the evaluation on ChatGPT?](https://mlsanity.com/paper/2303.12767/) (2023)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation](https://mlsanity.com/paper/2308.01240/) (2023)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning](https://mlsanity.com/paper/2307.10234/) (2023)
- [When do you need Chain-of-Thought Prompting for ChatGPT?](https://mlsanity.com/paper/2304.03262/) (2023)
- [MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection](https://mlsanity.com/paper/1606.03784/) (2016)
- [Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media](https://mlsanity.com/paper/2304.03087/) (2023)
- [Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations](https://mlsanity.com/paper/2006.00691/) (2020)

## Built on by (most cited first, 17 of 17)

- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions](https://mlsanity.com/paper/2406.12480/) (2024)
- [LLM Chain Ensembles for Scalable and Accurate Data Annotation](https://mlsanity.com/paper/2410.13006/) (2024)
- [Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance](https://mlsanity.com/paper/2403.00236/) (2024)
- [Exploring Vision Language Models for Multimodal and Multilingual Stance Detection](https://mlsanity.com/paper/2501.17654/) (2025)
- [Developing a Natural Language Understanding Model to Characterize Cable News Bias](https://mlsanity.com/paper/2310.09166/) (2023)
- [RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Science](https://mlsanity.com/paper/2408.08217/) (2024)
- [Exploiting contextual information to improve stance detection in informal political discourse with LLMs](https://mlsanity.com/paper/2602.04750/) (2026)
- [Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMA](https://mlsanity.com/paper/2402.06549/) (2024)
- [Journalism-Guided Agentic In-Context Learning for News Stance Detection](https://mlsanity.com/paper/2507.11049/) (2025)
- [Are Stereotypes Leading LLMs' Zero-Shot Stance Detection ?](https://mlsanity.com/paper/2510.20154/) (2025)
- [TruthStance: An Annotated Dataset of Conversations on Truth Social](https://mlsanity.com/paper/2602.14406/) (2026)
- [Debiasing Large Language Models toward Social Factors in Online Behavior Analytics through Prompt Knowledge Tuning](https://mlsanity.com/paper/2603.27057/) (2026)
- [Challenges in annotations by humans and LLMs: A case study of evaluative language](https://mlsanity.com/paper/2607.28119/) (2026)
- [Visual Framing for News Stance Detection via Image Generation](https://mlsanity.com/paper/2609.00685/) (2026)
- [Argument Structure Prediction in Online Conversations: A Comparative Study of Modeling Paradigms and Task Architectures](https://mlsanity.com/paper/2609.39225/) (2026)
- [StanceEval 2026: The Second Stance Detection Shared Task](https://mlsanity.com/paper/2610.03215/) (2026)

## Abstract (arXiv)

Stance classification, the task of predicting the viewpoint of an author on a subject of interest, has long been a focal point of research in domains ranging from social science to machine learning. Current stance detection methods rely predominantly on manual annotation of sentences, followed by training a supervised machine learning model. However, this manual annotation process requires laborious annotation effort, and thus hampers its potential to generalize across different contexts. In this work, we investigate the use of Large Language Models (LLMs) as a stance detection methodology that can reduce or even eliminate the need for manual annotations. We investigate 10 open-source models and 7 prompting schemes, finding that LLMs are competitive with in-domain supervised models but are not necessarily consistent in their performance. We also fine-tuned the LLMs, but discovered that fine-tuning process does not necessarily lead to better performance. In general, we discover that LLMs do not routinely outperform their smaller supervised machine learning models, and thus call for stance detection to be a benchmark for which LLMs also optimize for. The code used in this study is available at urlhttps://github.com/ijcruic/LLM-Stance-Labeling