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Aligning Language Models to User Opinions

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arxiv 2305.14929 v1 pith:A7KPB66E submitted 2023-05-24 cs.CL

EunJeong Hwang , Bodhisattwa Prasad Majumder , Niket Tandon This is my paper · ORCID
classification cs.CL
keywords useropinionsaligndemographicsllmsgroupideologicalideology
verification ladder T0 review T1 audit T2 compute T3 formal
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An important aspect of developing LLMs that interact with humans is to align models' behavior to their users. It is possible to prompt an LLM into behaving as a certain persona, especially a user group or ideological persona the model captured during its pertaining stage. But, how to best align an LLM with a specific user and not a demographic or ideological group remains an open question. Mining public opinion surveys (by Pew Research), we find that the opinions of a user and their demographics and ideologies are not mutual predictors. We use this insight to align LLMs by modeling both user opinions as well as user demographics and ideology, achieving up to 7 points accuracy gains in predicting public opinions from survey questions across a broad set of topics. In addition to the typical approach of prompting LLMs with demographics and ideology, we discover that utilizing the most relevant past opinions from individual users enables the model to predict user opinions more accurately.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models

    cs.CL 2026-07 conditional novelty 7.0 of 10

    LLM probability estimates violate the law of total probability across partitions, and subgroup-aggregated estimates often beat direct population-level estimates (the macro fallacy).

  2. Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A new dataset and benchmark maps movie clips to distributions of audience emotional reactions derived from YouTube comments, showing that finetuned vision-language models can predict these distributions from video alone.

  3. Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods

    cs.AI 2025-05 accept novelty 4.0 of 10

    LLMs extend, rather than replace, classical social science methods, with a proposed three-tier bias framework for LLM-augmented surveys.

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