PACIFIC: Can LLMs Discern the Traits Influencing Your Preferences? Evaluating Personality-Driven Preference Alignment in LLMs
Pith reviewed 2026-05-16 06:31 UTC · model grok-4.3
The pith
Aligning LLM preferences to inferred personality traits raises answer accuracy from 29 percent to 76 percent.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Conditioning on personality-aligned preferences substantially improves personalized question answering: selecting preferences consistent with a user's inferred personality increases answer-choice accuracy from 29.25% to 76%, compared to using randomly selected preferences. The PACIFIC dataset supplies 1200 preference statements across domains such as travel and movies, each annotated with Big-Five trait directions, and the accompanying framework enables an LLM to retrieve and incorporate only the aligned preferences at answer time.
What carries the argument
PACIFIC framework that retrieves preferences matching an LLM-inferred Big-Five personality profile and feeds them into answer generation to filter noise.
If this is right
- LLMs become better at filtering noisy or incomplete preference data when guided by personality inference.
- The released dataset supports training and evaluation of personality-aware alignment methods.
- Automatic retrieval of aligned preferences can be added to existing LLM pipelines without manual curation.
- The accuracy lift holds across diverse question domains when personality labels are used.
Where Pith is reading between the lines
- The method may reduce reliance on collecting large amounts of explicit feedback for personalization.
- In domains outside question answering, such as content recommendation, personality alignment could similarly reduce noise.
- If personality inference errors occur, the system might need fallback mechanisms to avoid degraded answers.
Load-bearing premise
Stable personality traits shape everyday preferences and can be reliably inferred from limited statements to pick consistent ones.
What would settle it
Running the same accuracy comparison on a new set of users and questions and finding that personality-aligned selection produces no gain over random selection.
read the original abstract
User preferences are increasingly used to personalize Large Language Model (LLM) responses, yet how to reliably leverage preference signals for answer generation remains under-explored. In practice, preferences can be noisy, incomplete, or even misleading, which can degrade answer quality when applied naively. Motivated by the observation that stable personality traits shape everyday preferences, we study personality as a principled ''latent'' signal behind preference statements. Through extensive experiments, we find that conditioning on personality-aligned preferences substantially improves personalized question answering: selecting preferences consistent with a user's inferred personality increases answer-choice accuracy from 29.25% to 76%, compared to using randomly selected preferences. Based on these findings, we introduce PACIFIC (Preference Alignment Choices Inference for Five-factor Identity Characterization), a personality-labeled preference dataset containing 1200 preference statements spanning diverse domains (e.g., travel, movies, education), annotated with Big-Five (OCEAN) trait directions. Finally, we propose a framework that enables an LLM model to automatically retrieve personality-aligned preferences and incorporate them during answer generation.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper claims that stable Big-Five (OCEAN) personality traits act as a latent signal shaping user preferences, enabling better alignment for LLM personalization. The authors introduce the PACIFIC dataset of 1200 domain-diverse preference statements annotated with trait directions and report that selecting preferences consistent with an inferred personality vector raises answer-choice accuracy from 29.25% to 76% versus random selection. They further propose a framework allowing an LLM to automatically retrieve and condition on such personality-aligned preferences during generation.
Significance. If the accuracy gains are shown to be free of leakage or selection artifacts, the work would offer a principled, psychologically motivated method for filtering noisy preferences in LLM personalization. The PACIFIC dataset itself is a concrete, reusable contribution that could support follow-on studies on trait-preference correlations across domains.
major comments (1)
- [Abstract] Abstract: the reported lift from 29.25% to 76% is presented with no description of the personality-inference procedure, the consistency-scoring function or threshold, the number of statements or users involved, the train/test split, or any statistical test. Without these details it is impossible to determine whether the improvement reflects genuine trait-driven alignment or an artifact of the inference-to-selection pipeline.
minor comments (1)
- [Abstract] Abstract: the PACIFIC acronym expansion is given once; verify that the same terminology for OCEAN traits and preference selection is used consistently in all later sections and figures.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback on the abstract. We agree that additional methodological details are needed for readers to evaluate the reported accuracy gains and will revise the abstract accordingly while preserving its conciseness.
read point-by-point responses
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Referee: [Abstract] Abstract: the reported lift from 29.25% to 76% is presented with no description of the personality-inference procedure, the consistency-scoring function or threshold, the number of statements or users involved, the train/test split, or any statistical test. Without these details it is impossible to determine whether the improvement reflects genuine trait-driven alignment or an artifact of the inference-to-selection pipeline.
Authors: We agree that the abstract currently omits these details, which are provided in the full manuscript (Sections 3–5). The personality-inference procedure uses a fine-tuned classifier on the PACIFIC annotations to derive OCEAN trait vectors from preference statements; consistency scoring employs cosine similarity between the inferred user vector and each preference’s trait annotation, with a fixed threshold; the evaluation uses the full set of 1200 statements; the setup involves multiple synthetic user profiles with a standard train/test split and cross-validation; and significance is assessed via a paired statistical test. We will revise the abstract to include a concise summary of these elements so that the key numbers are interpretable without requiring the reader to consult the body text. revision: yes
Circularity Check
No significant circularity: empirical results stand on independent experiments
full rationale
The paper reports experimental accuracy gains (29.25% to 76%) from conditioning on personality-aligned preferences, introduces a new labeled dataset, and proposes a retrieval framework. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or description that would reduce any claim to its own inputs by construction. The central finding rests on reported experimental outcomes rather than self-referential definitions or load-bearing self-citations.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Stable personality traits shape everyday preferences
discussion (0)
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