Multi-Perspective LLM Annotations for Valid Analyses in Subjective Tasks

• URL: http://arxiv.org/abs/2603.21404v1
• Date: Sun, 22 Mar 2026 21:18:05 GMT
• Title: Multi-Perspective LLM Annotations for Valid Analyses in Subjective Tasks
• Abstract summary: Perspective-Driven Inference treats the distribution of annotations across groups as the quantity of interest and estimates it using a small human annotation budget.<n>We evaluate on politeness and offensiveness rating tasks, showing targeted improvements for harder-to-model demographic groups.
• Score: 1.3490988186255934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models are increasingly used to annotate texts, but their outputs reflect some human perspectives better than others. Existing methods for correcting LLM annotation error assume a single ground truth. However, this assumption fails in subjective tasks where disagreement across demographic groups is meaningful. Here we introduce Perspective-Driven Inference, a method that treats the distribution of annotations across groups as the quantity of interest, and estimates it using a small human annotation budget. We contribute an adaptive sampling strategy that concentrates human annotation effort on groups where LLM proxies are least accurate. We evaluate on politeness and offensiveness rating tasks, showing targeted improvements for harder-to-model demographic groups relative to uniform sampling baselines, while maintaining coverage.

Related papers

• Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment [75.88231994139132]
  We show that even state-of-the-art models like GPT-4.1 achieve only 57.4% accuracy in predicting modal preferences.<n>We find significant pre-existing performance biases, where models better emulate young, male, Chinese, and Christian personas.
  arXiv  Detail & Related papers  (2026-04-14T15:06:13Z)
• Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation [89.52571224447111]
  Large language model (LLM) judges have often been used alongside traditional, algorithm-based metrics for tasks like summarization.<n>We provide an LLM judge bias analysis as a function of overlap with human-written responses in the domain of summarization.
  arXiv  Detail & Related papers  (2026-02-07T19:39:28Z)
• Enhancing LLM-Based Data Annotation with Error Decomposition [6.6544828402388445]
  Large language models offer a scalable alternative to human coding for data annotation tasks.<n>Their performance on subjective annotation tasks is less consistent and more prone to errors.<n>We propose a diagnostic evaluation paradigm that incorporates a human-in-the-loop step to separate task-inherent ambiguity from model-driven inaccuracies.
  arXiv  Detail & Related papers  (2026-01-17T05:43:17Z)
• Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks [8.246529401043128]
  We aim to explore how labeling decisions -- by both humans and LLMs -- can be statistically evaluated across individuals.<n>We develop a statistical evaluation method based on Krippendorff's $alpha$, paired bootstrapping, and the Two One-Sided t-Tests (TOST) equivalence test procedure.<n>We apply this approach to two datasets -- MovieLens 100K and PolitiFact -- and find that the LLM is statistically indistinguishable from a human annotator in the former.
  arXiv  Detail & Related papers  (2025-10-08T05:17:33Z)
• Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs [57.82819770709032]
  Large language models (LLMs) can be effective context-aided forecasters via na"ive direct prompting.<n>ReDP improves interpretability by eliciting explicit reasoning traces, allowing us to assess the model's reasoning over the context.<n>CorDP leverages LLMs solely to refine existing forecasts with context, enhancing their applicability in real-world forecasting pipelines.<n> IC-DP proposes embedding historical examples of context-aided forecasting tasks in the prompt, substantially improving accuracy even for the largest models.
  arXiv  Detail & Related papers  (2025-08-13T16:02:55Z)
• Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks [18.695435335031355]
  In subjective annotation tasks with multiple plausible answers, reviewing LLM outputs can change the label distribution.<n>We conducted a pre-registered experiment with 410 unique annotators and over 7,000 annotations.<n>We find that presenting crowdworkers with LLM-generated annotation suggestions did not make them faster, but did improve their self-reported confidence in the task.
  arXiv  Detail & Related papers  (2025-07-21T17:29:21Z)
• Can Reasoning Help Large Language Models Capture Human Annotator Disagreement? [84.32752330104775]
  Variation in human annotation (i.e., disagreements) is common in NLP.<n>We evaluate the influence of different reasoning settings on Large Language Model disagreement modeling.<n>Surprisingly, our results show that RLVR-style reasoning degrades performance in disagreement modeling.
  arXiv  Detail & Related papers  (2025-06-24T09:49:26Z)
• Utility-Focused LLM Annotation for Retrieval and Retrieval-Augmented Generation [96.18720164390699]
  This paper explores the use of large language models (LLMs) for annotating document utility in training retrieval and retrieval-augmented generation (RAG) systems.<n>Our results show that LLM-generated annotations enhance out-of-domain retrieval performance and improve RAG outcomes compared to models trained solely on human annotations or downstream QA metrics.
  arXiv  Detail & Related papers  (2025-04-07T16:05:52Z)
• Evaluating how LLM annotations represent diverse views on contentious topics [3.405231040967506]
  We show that generative large language models (LLMs) tend to be biased in the same directions on the same demographic categories within the same datasets.<n>We conclude with a discussion of the implications for researchers and practitioners using LLMs for automated data annotation tasks.
  arXiv  Detail & Related papers  (2025-03-29T22:53:15Z)
• Personas with Attitudes: Controlling LLMs for Diverse Data Annotation [4.916264341371062]
  We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs) We investigate whether personas increase annotation diversity and whether the impacts of individual personas on the resulting annotations are consistent and controllable.
  arXiv  Detail & Related papers  (2024-10-15T16:22:49Z)
• Hate Personified: Investigating the role of LLMs in content moderation [64.26243779985393]
  For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model's (LLM) ability to represent diverse groups is unclear. By including additional context in prompts, we analyze LLM's sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected.
  arXiv  Detail & Related papers  (2024-10-03T16:43:17Z)
• CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation [94.59630161324013]
  We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline.
  arXiv  Detail & Related papers  (2023-10-24T08:56:49Z)
• 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)<n>We feed annotators' Likert ratings and corresponding explanations into an LLM and prompt it to produce a numeric score anchored in a scoring rubric.<n>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)
• Benchmarking Large Language Models for News Summarization [79.37850439866938]
  Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. We find instruction tuning, and not model size, is the key to the LLM's zero-shot summarization capability.
  arXiv  Detail & Related papers  (2023-01-31T18:46:19Z)

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.