Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy (2025.findings-naacl)
Copied to clipboard
| Challenge: | Supervised methods are adept at text categorization, but dynamic nature of social media debates pose challenges for them . traditional methods for extracting themes from public discourse often reveal overarching patterns that might not capture specific nuances. |
| Approach: | They propose a generic approach that leverages the advanced capabilities of Large Language Models to extract latent arguments from social media messaging. |
| Outcome: | The proposed approach leverages the advanced capabilities of Large Language Models (LLMs) to extract latent arguments from social media messaging. |
Similar Papers
Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Climate change communication on social media increasingly employs microtargeting strategies to effectively reach and influence specific demographic groups. |
| Approach: | They analyze social media ads using large language models to examine their performance . they find that LLMs perform well overall, but certain biases exist . |
| Outcome: | The results show that LLMs perform well overall, but certain biases exist in certain demographic groups. |
Can LLMs Extract Frame-Semantic Arguments? (2025.emnlp-main)
Copied to clipboard
| Challenge: | Frame-semantic parsing is a critical task in natural language understanding . however, the ability of large language models to extract frame-sensical arguments remains unexplored . |
| Approach: | They propose a framework to extract frame-semantic arguments from large language models . they use JSON representations to enhance performance, but smaller models can achieve competitive results . |
| Outcome: | The proposed model achieves state-of-the-art on ambiguous targets while limiting generalization to out-of domain data. |
Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)
Copied to clipboard
| Challenge: | Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence. |
| Approach: | They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets . |
| Outcome: | The proposed model performs well on argument mining and argument generation tasks. |
LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to evaluate latent values and opinions in large language models suffer from three notable shortcomings. |
| Approach: | They propose to analyze 156k LLM responses to 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. |
| Outcome: | The proposed analysis of 156k LLM responses to the Political Compass Test (PCT) generated by 6 LLMs shows that tropes are recurrent and consistent across prompts. |
Exploiting contextual information to improve stance detection in informal political discourse with LLMs (2025.acl-srw)
Copied to clipboard
| Challenge: | Political stance detection is an increasingly relevant part of analyzing the flow of ideas in online environments where discourse is informal and implicitly expressed. |
| Approach: | They evaluate large language models for political stance detection in informal online discourse by analyzing user profiles derived from historical posts. |
| Outcome: | The proposed model improves accuracy by up to 74% on a political forum dataset. |
AI Argues Differently: Distinct Argumentative and Linguistic Patterns of LLMs in Persuasive Contexts (2025.emnlp-main)
Copied to clipboard
| Challenge: | Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, especially in high-stake settings such as persuasive online discourse. |
| Approach: | They propose to use general-purpose linguistic features and domain-specific features related to argument quality to compare human- and LLM-authored arguments. |
| Outcome: | The proposed framework compares arguments by humans and three LLMs using two easily-interpretable feature sets. |
Explicit Bayesian Inference to Uncover the Latent Themes of Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have impressive generative capabilities, yet their inner mechanisms remain largely opaque. |
| Approach: | They propose a variational autoencoder-based neural topic model to interpret LLMs generation process through an explicit Bayesian framework by inferring latent topic variables via variational inference. |
| Outcome: | The proposed model outperforms state-of-the-art topic models on intrinsic measures of coherence and diversity on multiple datasets and shows significant gains on classification and summarization tasks. |
Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show improvements on Reddit and Twitter data . |
| Approach: | They propose to take advantage of Large Language Models (LLMs) to better identify user communities. |
| Outcome: | The proposed model improves on Reddit and Twitter data and tasks of community detection, bot detection, and news media profiling. |
Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations (2024.acl-long)
Copied to clipboard
| Challenge: | Existing models for language from a social perspective are gaining popularity . we present a generalizable classification approach that leverages Large Language Models . |
| Approach: | They propose a generalizable classification approach that leverages Large Language Models to detect social meaning in conversations. |
| Outcome: | The proposed approach improves on two social meaning detection tasks over 2,340 settings. |
Argument Mining with Fine-Tuned Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Argument Mining (AM) pipelines use fine-tuned large language models (LLMs) . initial approaches employ supervised machine learning algorithms, such as Maximum Entropy classifiers and Logistic Regressions. |
| Approach: | They propose to model the three main AM sub-tasks as text generation tasks and fine-tune eight popular quantized and non-quantized large language models (LLMs) on the benchmark PE, AbstRCT, and CDCP datasets. |
| Outcome: | The proposed pipeline achieves state-of-the-art across all AM sub-tasks and datasets, showing significant improvements over previous benchmarks. |