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Research Paper#LLMs, Social Desirability Bias, Prompt Engineering, Silicon Sampling🔬 ResearchAnalyzed: Jan 3, 2026 19:41

Mitigating Social Bias in LLM-Based Population Simulations

Published:Dec 27, 2025 23:21
1 min read
ArXiv

Analysis

This paper addresses a crucial problem in the use of Large Language Models (LLMs) for simulating population responses: Social Desirability Bias (SDB). It investigates prompt-based methods to mitigate this bias, which is essential for ensuring the validity and reliability of LLM-based simulations. The study's focus on practical prompt engineering makes the findings directly applicable to researchers and practitioners using LLMs for social science research. The use of established datasets like ANES and rigorous evaluation metrics (Jensen-Shannon Divergence) adds credibility to the study.
Reference

“Reformulated prompts most effectively improve alignment by reducing distribution concentration on socially acceptable answers and achieving distributions closer to ANES.”

PermalinkArXiv
Research#llm👥 CommunityAnalyzed: Dec 28, 2025 21:57

Practical Methods to Reduce Bias in LLM-Based Qualitative Text Analysis

Published:Dec 25, 2025 12:29
1 min read
r/LanguageTechnology

Analysis

The article discusses the challenges of using Large Language Models (LLMs) for qualitative text analysis, specifically the issue of priming and feedback-loop bias. The author, using LLMs to analyze online discussions, observes that the models tend to adapt to the analyst's framing and assumptions over time, even when prompted for critical analysis. The core problem is distinguishing genuine model insights from contextual contamination. The author questions current mitigation strategies and seeks methodological practices to limit this conversational adaptation, focusing on reliability rather than ethical concerns. The post highlights the need for robust methods to ensure the validity of LLM-assisted qualitative research.
Reference

“Are there known methodological practices to limit conversational adaptation in LLM-based qualitative analysis?”

Permalinkr/LanguageTechnology
Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 13:53

Personality Infusion Mitigates Priming in LLM Relevance Judgments

Published:Nov 29, 2025 08:37
1 min read
ArXiv

Analysis

This research explores a novel approach to improve the reliability of large language models in evaluating relevance, which is crucial for information retrieval. The study's focus on mitigating priming effects through personality infusion is a significant contribution to the field.
Reference

“The study aims to mitigate the threshold priming effect in large language model-based relevance judgments.”

PermalinkArXiv