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HFEPX · Eval paper review

A Jury, Not an Electorate: Label-Free Reliability Estimation for Ensembles of Language Models

Alessandro Rozza, Davide Mambelli, Steven Motta, Juri Taresch

Published

Oct 15, 2026

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

15% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Oct 16, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
0/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

An ensemble of language models is a jury, not an electorate. The answers are testimony from witnesses of unknown reliability, and the question is not what the majority said but who is credible. Most ensemble methods assign each expert a fixed reputation, often learned from labels. We ask whether it can instead be inferred from the pattern of agreement and disagreement among the experts. We model the experts as a jury: each question has a latent true answer, each expert reports it with unknown reliability, and those reliabilities are estimated by Expectation Maximisation (EM) from unlabelled answer configurations alone. We show that its decision rule reduces to a log-odds weighted linear score over the experts' answer distributions. What changes is not how the jury votes, but how it learns whom to trust. On a 1560-question benchmark, the jury, with nine experts of up to 3B parameters, performs comparably to the same fusion rule with label-fitted weights, showing no statistically significant advantage from supervision. Instead, it outperforms the best single model with matched self-consistency and the strongest label-free alternative on the full benchmark.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"An ensemble of language models is a jury, not an electorate."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"An ensemble of language models is a jury, not an electorate."

Quality Controls

missing

Not reported

No explicit QC controls found.

"An ensemble of language models is a jury, not an electorate."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"An ensemble of language models is a jury, not an electorate."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"An ensemble of language models is a jury, not an electorate."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Most ensemble methods assign each expert a fixed reputation, often learned from labels."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Domain Experts
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

An ensemble of language models is a jury, not an electorate.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • An ensemble of language models is a jury, not an electorate.
  • The answers are testimony from witnesses of unknown reliability, and the question is not what the majority said but who is credible.
  • Most ensemble methods assign each expert a fixed reputation, often learned from labels.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Expert verification evaluation Agreement / Kappa

Contribution summary

  • We show that its decision rule reduces to a log-odds weighted linear score over the experts' answer distributions.
  • On a 1560-question benchmark, the jury, with nine experts of up to 3B parameters, performs comparably to the same fusion rule with label-fitted weights, showing no statistically significant advantage from supervision.
  • Instead, it outperforms the best single model with matched self-consistency and the strongest label-free alternative on the full benchmark.

Why it matters for eval

  • On a 1560-question benchmark, the jury, with nine experts of up to 3B parameters, performs comparably to the same fusion rule with label-fitted weights, showing no statistically significant advantage from supervision.
  • Instead, it outperforms the best single model with matched self-consistency and the strongest label-free alternative on the full benchmark.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.