Introduces HumanStudy-Bench to evaluate LLM agents against 12 replicated human behavioral studies, finding agent design affects alignment more than model scale with polarized outcomes.
PersonaLLM: Investigating the ability of large language models to express personality traits
9 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 9representative citing papers
Steering language models with intermittent implicit trait reinforcements reduces misalignment contagion in multi-agent social dilemma games more effectively than system prompt repetition.
Mean-difference residual stream injections outperform personality prompting for OCEAN trait steering in most LLMs, with hybrids performing best and showing approximate linearity but non-human trait covariances.
Agreeableness in AI personas reliably predicts sycophantic behavior in 9 of 13 tested language models.
ETI lets LLM agents infer and track partners' psychological traits (warmth and competence) from histories, cutting payoff loss 45-77% in games and boosting performance 3-29% on MultiAgentBench versus CoT baselines.
PeReGrINE is a graph-based benchmark that restructures Amazon Reviews 2023 with temporal cutoffs and introduces dissonance analysis to measure how well retrieval-conditioned models match user style and product consensus.
GPT produces click distributions significantly different from real humans in 53% of UX first-click tasks, with prompting techniques like personas and chain-of-thought failing to improve alignment.
High agreeableness in LLM voice assistants increases older adults' empathy perceptions and real-time explanations outperform history-based ones, but personality does not affect perceived intelligence.
Medium personality expression in LLM agents yields the most positive user perceptions in goal-oriented tasks, further improved by trait alignment.
citing papers explorer
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Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks
Medium personality expression in LLM agents yields the most positive user perceptions in goal-oriented tasks, further improved by trait alignment.