mlsanityData to 14 Oct 2026

Language Models Learn Metadata: Political Stance Detection Case Study

arXiv 2409.13756 ↗First posted 15 Sep 2024cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Stance detection is a crucial NLP task with numerous applications in social science, from analyzing online discussions to assessing political campaigns. This paper investigates the optimal way to incorporate metadata into a political stance detection task. We demonstrate that previous methods combining metadata with language-based data for political stance detection have not fully utilized the metadata information; our simple baseline, using only party membership information, surpasses the current state-of-the-art. We then show that prepending metadata (e.g., party and policy) to political speeches performs best, outperforming all baselines, indicating that complex metadata inclusion systems may not learn the task optimally.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 46 references that are in the map. First posted on arXiv 15 Sep 2024.

Builds on

14 of 46 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  5. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  6. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 981
  7. 2021Few-shot Learning with Multilingual Language Modelscited by 278
  8. 2021A Survey on Stance Detection for Mis- and Disinformation Identificationcited by 75
  9. 2024From LLM to NMT: Advancing Low-Resource Machine Translation with Claudecited by 58
  10. 2018Topic-Specific Sentiment Analysis Can Help Identify Political Ideologycited by 10
  11. 2018Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Predictioncited by 4
  12. 2024Dynamic Few-Shot Learning for Knowledge Graph Question Answeringcited by 4
2 more
  1. 2024Zero-shot and Few-shot Generation Strategies for Artificial Clinical Recordscited by 2
  2. 2022Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysiscited by 1

Built on by

Not cited yet.