Hate Personified: Investigating the role of LLMs in content moderation

• URL: http://arxiv.org/abs/2410.02657v1
• Date: Thu, 3 Oct 2024 16:43:17 GMT
• Title: Hate Personified: Investigating the role of LLMs in content moderation
• Authors: Sarah Masud, Sahajpreet Singh, Viktor Hangya, Alexander Fraser, Tanmoy Chakraborty,
• Abstract summary: 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.
• Score: 64.26243779985393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 comprehensively analyze LLM's sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected. Our findings on two LLMs, five languages, and six datasets reveal that mimicking persona-based attributes leads to annotation variability. Meanwhile, incorporating geographical signals leads to better regional alignment. We also find that the LLMs are sensitive to numerical anchors, indicating the ability to leverage community-based flagging efforts and exposure to adversaries. Our work provides preliminary guidelines and highlights the nuances of applying LLMs in culturally sensitive cases.

Related papers

• Large Language Models Reflect the Ideology of their Creators [73.25935570218375]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. We uncover notable diversity in the ideological stance exhibited across different LLMs and languages.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse. Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-AI collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• Understanding the Role of LLMs in Multimodal Evaluation Benchmarks [77.59035801244278]
  This paper investigates the role of the Large Language Model (LLM) backbone in Multimodal Large Language Models (MLLMs) evaluation. Our study encompasses four diverse MLLM benchmarks and eight state-of-the-art MLLMs. Key findings reveal that some benchmarks allow high performance even without visual inputs and up to 50% of error rates can be attributed to insufficient world knowledge in the LLM backbone.
  arXiv  Detail & Related papers  (2024-10-16T07:49:13Z)
• 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)
• Do Large Language Models Possess Sensitive to Sentiment? [18.88126980975737]
  Large Language Models (LLMs) have recently displayed their extraordinary capabilities in language understanding. This paper investigates the ability of LLMs to detect and react to sentiment in text modal.
  arXiv  Detail & Related papers  (2024-09-04T01:40:20Z)
• RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models [12.112914393948415]
  We present RUPBench, a benchmark designed to evaluate large language models (LLMs) across diverse reasoning tasks. Our benchmark incorporates 15 reasoning datasets, categorized into commonsense, arithmetic, logical, and knowledge-intensive reasoning. By examining the performance of state-of-the-art LLMs such as GPT-4o, Llama3, Phi-3, and Gemma on both original and perturbed datasets, we provide a detailed analysis of their robustness and error patterns.
  arXiv  Detail & Related papers  (2024-06-16T17:26:44Z)
• Harnessing Artificial Intelligence to Combat Online Hate: Exploring the Challenges and Opportunities of Large Language Models in Hate Speech Detection [4.653571633477755]
  Large language models (LLMs) excel in many diverse applications beyond language generation, e.g., translation, summarization, and sentiment analysis. This becomes pertinent in the realm of identifying hateful or toxic speech -- a domain fraught with challenges and ethical dilemmas.
  arXiv  Detail & Related papers  (2024-03-12T19:12:28Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
  This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks. We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
  arXiv  Detail & Related papers  (2023-05-24T10:45:25Z)

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.