Passing Coarse Marginal Checks Can Be Cheap: Persona Mixtures and Imprecise Treatment-Response Estimates in an LLM Persona Panel
- URL: http://arxiv.org/abs/2608.00979v1
- Date: Sun, 02 Aug 2026 04:10:11 GMT
- Title: Passing Coarse Marginal Checks Can Be Cheap: Persona Mixtures and Imprecise Treatment-Response Estimates in an LLM Persona Panel
- Abstract summary: We study a fixed panel of sixteen lightweight persona-conditioned GPT-4.1 configurations in repeated strategic games.<n>Variation was strongly prompt-indexed, but its share depended on uncertainty assumptions.<n>Results concern one fixed model-prompt panel and do not establish human substitutability.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models are increasingly used as synthetic research participants and are often validated by whether their marginal responses resemble human data. We study a fixed panel of sixteen lightweight persona-conditioned GPT-4.1 configurations in repeated strategic games. The panel met preregistered broad-reference condition-mean criteria in three of four repeated-game cells; the sole miss was 0.011 below the lower reference bound. Variation was strongly prompt-indexed, but its share depended on uncertainty assumptions: fixed-panel symmetric-Dirichlet sensitivities produced median between-prompt shares of 63%-71% under Jeffreys alpha=0.5 and 47%-53% under alpha=1, while finite-opportunity plug-in estimates were 85%-96%. Aggregate continuation-probability contrasts were +0.083 and +0.078, with conservative simultaneous 95% intervals [-0.171, +0.330] and [-0.181, +0.330]. The treatment jointly changed the continuation process and its textual representation. A separate wording-and-position operation shifted cooperation from 0/40 to 37/40 in the bare configuration, and a label conflict also revealed representation control. The original persona-level p13 result was not prospectively family-controlled, while a post-adjudication exact gate was structurally underpowered; p13 is therefore a replication target rather than a finding. External review exposed family-error, dependence, construct, and boundary-uncertainty defects, and zero-call reanalysis changed the interpretation without rewriting the historical record. The registered marginal criteria could be passed without precisely estimating the treatment-response object. A public capsule verifies 4,916 confirmatory Phase 3-5 runs with no live model calls. The results concern one fixed model-prompt panel and do not establish human substitutability.
Related papers
- Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts, Support Collapse and Conditioning Failure [51.736723807086385]
Population-marginal agreement is evidence about an elicitation contract and an estimand obtainable without simulated respondents, not evidence of individual simulation.<n>On nine aligned model-battery pairs, a no-persona population-prevalence query averages 6.27 MAE versus 12.39 for committed panels and wins all nine comparisons.
arXiv Detail & Related papers (2026-09-07T10:22:22Z) - Peer-Voted LLM-Agent Stress Tests Find Feed-Induced Lexical Convergence but No Reliable Matched-Exposure Advantage for Distributed Sources [0.013714053458441644]
We introduce PV-SST, a peer-voted social-platform testbed.<n>Experiment comprises 448 trials and 112 complete model-by-topic-by-seed blocks.
arXiv Detail & Related papers (2026-08-20T12:01:19Z) - Blind to the Pivotal Vote: Aggregate Independence Metrics Miss Where Verification Actually Helps [9.710464466895521]
We show that a majority-side replacement rule raises overall accuracy from 82.44% to 85.62% while invoking the signal on 16.2% of queries.<n>On HumanEval+/MBPP+, a majority-side replacement rule raises overall accuracy from 82.44% to 85.62% while invoking the signal on 16.2% of queries.
arXiv Detail & Related papers (2026-08-07T08:14:46Z) - Distribution-First Population Simulation: Collapse, Calibration, and Recall in Non-WEIRD LLM Persona Modeling [0.0]
Synthetic-population tools run every individual as an independent large language model (LLM) agent.<n>We show that this paradigm has a basic failure mode, and we set a distribution-first corrective against it.
arXiv Detail & Related papers (2026-07-17T15:45:57Z) - Scaffold splits hide structural-frontier failures in ADMET models [25.408600489964726]
We introduce a label-free structural-frontier split that reserves the sparsest and most physicochemically remote scaffold groups.<n>We evaluate it on six public experimental or curated ADMET tasks.
arXiv Detail & Related papers (2026-07-12T12:25:50Z) - Multimodal Evaluator Preference Collapse: Cross-Modal Contagion in Self-Evolving Agents [1.0152838128195467]
We show that Evaluator Preference Collapse ( EPC) is dramatically amplified in multimodal settings.<n>We then demonstrate a novel phenomenon we term cross-modal contagion.<n>We identify cross-model evaluator architecture as the primary risk factor for preference drift.
arXiv Detail & Related papers (2026-06-15T13:18:20Z) - Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking [22.825786049667602]
We propose a replication-first paradigm to anchor validity to a single human-rater consensus.<n>We certify the instrument via four properties -- reliability across K runs, cross-instrument replication across architecturally distinct judges, historical-footprint calibration via judges from earlier training cohorts, and pre-registered prediction.<n>We test it on emotional accompaniment by letting the self-evolve data-driven across: the dimensions are not pre-stipulated and the procedure stabilizes to a 9-dimension set.
arXiv Detail & Related papers (2026-05-27T03:41:11Z) - Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
arXiv Detail & Related papers (2026-05-15T17:08:27Z) - The Autocorrelation Blind Spot: Why 42% of Turn-Level Findings in LLM Conversation Analysis May Be Spurious [35.76482964927589]
We characterize the autocorrelation structure of 66 turn-level metrics across 202 multi-turn conversations.<n>We show that 42% of associations that appear significant under standard pooled testing fail to survive cluster-robust correction.<n>We present a two-stage correction framework combining Chelton (1983) effective degrees of freedom with conversation-level block bootstraps.
arXiv Detail & Related papers (2026-04-15T20:54:39Z) - From Black Box to Glass Box: Cross-Model ASR Disagreement to Prioto Review in Ambient AI Scribe Documentation [43.148402136307716]
Cross-model disagreement among heterogeneous ASR systems can act as a reference-free uncertainty signal.<n>We transcribed 50 publicly available medical audio clips with eight ASR systems spanning commercial APIs and open-source engines.<n>Low-agreement regions were enriched for content disagreements, with the content fraction increasing from 53.9% to 73.9% across quintiles of high-risk mass.
arXiv Detail & Related papers (2026-03-02T13:02:13Z) - PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology [48.732366302949515]
Large language models (LLMs) have achieved expert-level performance on standardized examinations, yet multiple-choice accuracy poorly reflects real-world clinical utility and safety.<n>We developed a human-in-the-loop pipeline to create expert rubrics for de-identified patient questions.<n>We evaluated 22 proprietary and open-source LLMs using an LLM-as-a-judge framework, measuring clinical completeness, factual accuracy, and web-search integration.
arXiv Detail & Related papers (2026-03-02T00:50:39Z) - Causal Understanding by LLMs: The Role of Uncertainty [43.87879175532034]
Recent papers show LLMs achieve near-random accuracy in causal relation classification.<n>We investigate whether pretraining exposure to causal examples improves causal understanding.
arXiv Detail & Related papers (2025-09-24T13:06:35Z) - Fact or Facsimile? Evaluating the Factual Robustness of Modern Retrievers [34.31192184496381]
Dense retrievers and rerankers are central to retrieval-augmented generation (RAG) pipelines.<n>We evaluate how much factual competence these components inherit or lose from large language models (LLMs) they are based on.<n>For every embedding model, cosine-similarity scores between queries and correct completions are significantly higher than those for incorrect ones.
arXiv Detail & Related papers (2025-08-28T04:13:51Z) - An Auditable Pipeline for Fuzzy Full-Text Screening in Systematic Reviews: Integrating Contrastive Semantic Highlighting and LLM Judgment [0.0]
Full-text screening is the major bottleneck of systematic reviews.<n>We present a scalable, auditable pipeline that reframes inclusion/exclusion as a fuzzy decision problem.
arXiv Detail & Related papers (2025-08-17T17:41:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.