Bridging the Gap: In-Context Learning for Modeling Human Disagreement

• URL: http://arxiv.org/abs/2506.06113v1
• Date: Fri, 06 Jun 2025 14:24:29 GMT
• Title: Bridging the Gap: In-Context Learning for Modeling Human Disagreement
• Authors: Benedetta Muscato, Yue Li, Gizem Gezici, Zhixue Zhao, Fosca Giannotti,
• Abstract summary: Large Language Models (LLMs) have shown strong performance on NLP classification tasks.<n>This study examines whether LLMs can capture multiple perspectives and reflect annotator disagreement in subjective tasks such as hate speech and offensive language detection.
• Score: 8.011316959982654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown strong performance on NLP classification tasks. However, they typically rely on aggregated labels-often via majority voting-which can obscure the human disagreement inherent in subjective annotations. This study examines whether LLMs can capture multiple perspectives and reflect annotator disagreement in subjective tasks such as hate speech and offensive language detection. We use in-context learning (ICL) in zero-shot and few-shot settings, evaluating four open-source LLMs across three label modeling strategies: aggregated hard labels, and disaggregated hard and soft labels. In few-shot prompting, we assess demonstration selection methods based on textual similarity (BM25, PLM-based), annotation disagreement (entropy), a combined ranking, and example ordering strategies (random vs. curriculum-based). Results show that multi-perspective generation is viable in zero-shot settings, while few-shot setups often fail to capture the full spectrum of human judgments. Prompt design and demonstration selection notably affect performance, though example ordering has limited impact. These findings highlight the challenges of modeling subjectivity with LLMs and the importance of building more perspective-aware, socially intelligent models.

Related papers

• Revisiting LLM Value Probing Strategies: Are They Robust and Expressive? [81.49470136653665]
  We evaluate the robustness and expressiveness of value representations across three widely used probing strategies.<n>We show that the demographic context has little effect on the free-text generation, and the models' values only weakly correlate with their preference for value-based actions.
  arXiv  Detail & Related papers  (2025-07-17T18:56:41Z)
• Evaluating how LLM annotations represent diverse views on contentious topics [3.405231040967506]
  We show how generative large language models (LLMs) represent diverse viewpoints on contentious labeling tasks.<n>Our findings suggest that when using LLMs to annotate data, under-representing the views of particular groups is not a substantial concern.
  arXiv  Detail & Related papers  (2025-03-29T22:53:15Z)
• Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement [22.992484902761994]
  This study systematically evaluates the performance of multiple Large Language Models (LLMs) in detecting offensive language.<n>We analyze binary classification accuracy, examine the relationship between model confidence and human disagreement, and explore how disagreement samples influence model decision-making.
  arXiv  Detail & Related papers  (2025-02-10T07:14:26Z)
• What Makes In-context Learning Effective for Mathematical Reasoning: A Theoretical Analysis [81.15503859645149]
  In this paper, we aim to theoretically analyze the impact of in-context demonstrations on large language models' reasoning performance.<n>We propose a straightforward, generalizable, and low-complexity demonstration selection method named LMS3.
  arXiv  Detail & Related papers  (2024-12-11T11:38:11Z)
• Diverging Preferences: When do Annotators Disagree and do Models Know? [92.24651142187989]
  We develop a taxonomy of disagreement sources spanning 10 categories across four high-level classes. We find that the majority of disagreements are in opposition with standard reward modeling approaches. We develop methods for identifying diverging preferences to mitigate their influence on evaluation and training.
  arXiv  Detail & Related papers  (2024-10-18T17:32:22Z)
• Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning [41.606494950216764]
  In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs) This paper decomposes the overall performance of ICL into three dimensions, label space, format, and discrimination. We show that ICL exhibits significant efficacy in regulating the label space and format, which helps LLMs respond to desired label words.
  arXiv  Detail & Related papers  (2024-04-11T08:20:10Z)
• Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blinds [59.71218039095155]
  We evaluate language understanding capacities on simple inference tasks that most humans find trivial. We target (i) grammatically-specified entailments, (ii) premises with evidential adverbs of uncertainty, and (iii) monotonicity entailments. The models exhibit moderate to low performance on these evaluation sets.
  arXiv  Detail & Related papers  (2023-05-24T06:41:09Z)
• Using Natural Language Explanations to Rescale Human Judgments [81.66697572357477]
  We propose a method to rescale ordinal annotations and explanations using large language models (LLMs) We feed annotators' Likert ratings and corresponding explanations into an LLM and prompt it to produce a numeric score anchored in a scoring rubric. Our method rescales the raw judgments without impacting agreement and brings the scores closer to human judgments grounded in the same scoring rubric.
  arXiv  Detail & Related papers  (2023-05-24T06:19:14Z)
• Active Learning Principles for In-Context Learning with Large Language Models [65.09970281795769]
  This paper investigates how Active Learning algorithms can serve as effective demonstration selection methods for in-context learning. We show that in-context example selection through AL prioritizes high-quality examples that exhibit low uncertainty and bear similarity to the test examples.
  arXiv  Detail & Related papers  (2023-05-23T17:16:04Z)
• Contrastive Learning for Prompt-Based Few-Shot Language Learners [14.244787327283335]
  We present a contrastive learning framework that clusters inputs from the same class under different augmented "views" We create different "views" of an example by appending it with different language prompts and contextual demonstrations. Our method can improve over the state-of-the-art methods in a diverse set of 15 language tasks.
  arXiv  Detail & Related papers  (2022-05-03T04:56:45Z)

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.