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HFEPX · Eval paper review

JudgeMoE: Distributional Aggregation for LLM-as-a-Judge

Yiqi Liu, Joseph James, Yang Wang, Kun Zhao +2 more

Published

Oct 5, 2026

Citations

0

Trust level

Low

Usefulness score

2/100 (Low)

Extraction confidence

35% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 5, 2026

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Tags

Llm As Judge

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

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

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
Detected
Eval setup described
Usefulness for eval research
2/100
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

When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression. We introduce JudgeMoE, a lightweight aggregator that assigns example-specific weights to cached judge score distributions and fuses them before computing a final score. A protocol study shows that score-range choice is unstable across judge--dataset settings and that soft scoring usually outperforms hard decoding. On the original 10-cell benchmark, JudgeMoE improves mean Spearman over uniform log pooling by $+0.079$. Applying the same configuration to six additional cells yields a $+0.0393$ mean gain over the strongest local single judge across 16 cells, with positive differences in 12/16 cells and a one-sided Wilcoxon signed-rank $p=0.0091$. Validation-based analyses further show that the preferred aggregation method depends on the task and judge pool.

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.

"When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression."

Evaluation Modes

partial

Llm As Judge

Includes extracted eval setup.

"When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression."

Quality Controls

missing

Not reported

No explicit QC controls found.

"When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression."

Reported Metrics

partial

Spearman

Useful for evaluation criteria comparison.

"On the original 10-cell benchmark, JudgeMoE improves mean Spearman over uniform log pooling by $+0.079$."

Benchmarks and datasets

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

Reported metrics

spearman
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Unit of annotation
Scalar (inferred)
Expertise required
General
Evaluation details
Evaluation modes
Llm As Judge
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression.

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

Key takeaways

  • When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression.
  • We introduce JudgeMoE, a lightweight aggregator that assigns example-specific weights to cached judge score distributions and fuses them before computing a final score.
  • A protocol study shows that score-range choice is unstable across judge--dataset settings and that soft scoring usually outperforms hard decoding.

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

Contribution summary

  • When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression.
  • We introduce JudgeMoE, a lightweight aggregator that assigns example-specific weights to cached judge score distributions and fuses them before computing a final score.
  • A protocol study shows that score-range choice is unstable across judge--dataset settings and that soft scoring usually outperforms hard decoding.

Why it matters for eval

  • When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression.
  • We introduce JudgeMoE, a lightweight aggregator that assigns example-specific weights to cached judge score distributions and fuses them before computing a final score.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Llm As Judge

  • 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

    Detected: spearman