mlsanityData to 14 Oct 2026

Advancing Harmful Content Detection in Organizational Research: Integrating Large Language Models with Elo Rating System

arXiv 2506.16575 ↗First posted 19 Jun 2025cs.AI cs.CLcited by 1

On the map: LLM safety and security → Safety alignment, refusal and guardrails

Abstract (arXiv)

Large language models (LLMs) offer promising opportunities for organizational research. However, their built-in moderation systems can create problems when researchers try to analyze harmful content, often refusing to follow certain instructions or producing overly cautious responses that undermine validity of the results. This is particularly problematic when analyzing organizational conflicts such as microaggressions or hate speech. This paper introduces an Elo rating-based method that significantly improves LLM performance for harmful content analysis In two datasets, one focused on microaggression detection and the other on hate speech, we find that our method outperforms traditional LLM prompting techniques and conventional machine learning models on key measures such as accuracy, precision, and F1 scores. Advantages include better reliability when analyzing harmful content, fewer false positives, and greater scalability for large-scale datasets. This approach supports organizational applications, including detecting workplace harassment, assessing toxic communication, and fostering safer and more inclusive work environments.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 13 of its 40 references that are in the map. First posted on arXiv 19 Jun 2025.

Builds on

13 of 40 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024OR-Bench: An Over-Refusal Benchmark for Large Language Models31 May

Other references · 12

  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. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2020Scaling Laws for Neural Language Modelscited by 6,586
  5. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
  6. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
  7. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 606
  8. 2024SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusalcited by 240
  9. 2019Text Classification Algorithms: A Surveycited by 163
  10. 2024Large Language Models Meet NLP: A Surveycited by 103
  11. 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
  12. 2024Political-LLM: Large Language Models in Political Sciencecited by 27

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Multi-Dimensional Comparative Scale Construction for Efficient Personalized Subjective Judgment in High-Traffic Applicationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Supporting Human Raters with the Detection of Harmful Content using Large Language Models
  2. 2025Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs
  3. 2025Can LLMs Rank the Harmfulness of Smaller LLMs? We are Not There Yet
  4. 2025HarmMetric Eval: Benchmarking Metrics and Judges for LLM Harmfulness Assessment
  5. 2024Unboxing Occupational Bias: Grounded Debiasing of LLMs with U.S. Labor Data
  6. 2025What Are They Filtering Out? An Experimental Benchmark of Filtering Strategies for Harm Reduction in Pretraining Datasets
  7. 2023Down the Toxicity Rabbit Hole: A Novel Framework to Bias Audit Large Language Models
  8. 2026HarmProfile: Characterizing Harmful Distributions in Frontier LLMs