Fine-tuning LLMs on small pilot survey data balances structural, marginal, and individual fidelity better than prompting or rectification, but fidelity levels vary across subsamples in a COVID-19 misinformation case study.
Lar ge language models cannot replace human participants because they cannot portray identity gr oups
4 Pith papers cite this work. Polarity classification is still indexing.
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Simple supervision improves LLM distributional alignment with diverse population groups on three datasets, with evaluation across multiple models and prompts providing a benchmark.
Case study of grassroots participatory design in US AI policymaking for marginalized communities, documenting engagement challenges and offering recommendations.
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.
citing papers explorer
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Beyond the Mean: Three-Axis Fidelity for Aligning LLM-Based Survey Simulators from Small Pilot Data
Fine-tuning LLMs on small pilot survey data balances structural, marginal, and individual fidelity better than prompting or rectification, but fidelity levels vary across subsamples in a COVID-19 misinformation case study.
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Improving the Distributional Alignment of LLMs using Supervision
Simple supervision improves LLM distributional alignment with diverse population groups on three datasets, with evaluation across multiple models and prompts providing a benchmark.
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Challenges to Grassroots Organization Engagement with AI Policy
Case study of grassroots participatory design in US AI policymaking for marginalized communities, documenting engagement challenges and offering recommendations.
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Bias in Large Language Models: Origin, Evaluation, and Mitigation
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.