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HFEPX Hub

Llm As Judge + General (Last 60 Days)

Updated from current HFEPX corpus (Apr 12, 2026). 29 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 29 papers are grouped in this hub page. Common evaluation modes: Llm As Judge, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Adjudication. Frequently cited benchmark: ALFWorld. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Mar 22, 2026.

Papers: 29 Last published: Mar 22, 2026 Global RSS Tag RSS
Llm As JudgeGeneralLast 60d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Medium .

High-Signal Coverage

100.0%

29 / 29 sampled papers are not low-signal flagged.

Replication-Ready Set

5

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 5 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 1 papers support judge-vs-human agreement analysis.
  • 1 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

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Why This Matters For Eval Research

  • 44.8% of papers report explicit human-feedback signals, led by pairwise preferences.
  • LLM-as-judge appears in 100% of papers in this hub.
  • ALFWorld is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is adjudication (3.4% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.

Benchmark Interpretation

  • ALFWorld appears in 3.4% of hub papers (1/29); use this cohort for benchmark-matched comparisons.
  • DROP appears in 3.4% of hub papers (1/29); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 24.1% of hub papers (7/29); compare with a secondary metric before ranking methods.
  • coherence is reported in 10.3% of hub papers (3/29); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (44.8% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (3.4% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (31% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (55.2% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (13.8% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (44.8% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 31% of papers.

Known Gaps

  • Only 3.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (13.8% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (ALFWorld vs DROP) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and coherence.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

Yes Human Eval , Llm As Judge WebArena , ToolBench Precision , Pass@1 Not Reported
IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation

Mar 5, 2026

Yes Llm As Judge If Rewardbench Not Reported Not Reported
HyperMem: Hypergraph Memory for Long-Term Conversations

Apr 9, 2026

Yes Llm As Judge , Automatic Metrics Not Reported Accuracy , Coherence Not Reported
Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models

Mar 26, 2026

Yes Llm As Judge Not Reported Latency Not Reported
LLM-as-a-Judge for Time Series Explanations

Apr 2, 2026

No
Not Reported
Llm As Judge , Automatic Metrics DROP Accuracy , Faithfulness Not Reported
Weakly Supervised Distillation of Hallucination Signals into Transformer Representations

Apr 7, 2026

No
Not Reported
Llm As Judge , Automatic Metrics SQuAD F1 , Latency Not Reported
Beyond the Illusion of Consensus: From Surface Heuristics to Knowledge-Grounded Evaluation in LLM-as-a-Judge

Mar 11, 2026

Yes Llm As Judge Not Reported Spearman Not Reported
InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

Feb 16, 2026

No
Not Reported
Llm As Judge Innoeval Not Reported Adjudication
Reward Prediction with Factorized World States

Mar 10, 2026

No
Not Reported
Llm As Judge , Simulation Env ALFWorld Success rate Not Reported
ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts

Mar 5, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Thaisafetybench F1 , F1 weighted Not Reported
EvoIdeator: Evolving Scientific Ideas through Checklist-Grounded Reinforcement Learning

Mar 23, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported
Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge

Apr 7, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal AgentHER: Hindsight Experience Replay for LLM Agent… IF-RewardBench: Benchmarking Judge Models for Instr… HyperMem: Hypergraph Memory for Long-Term Conversat…
Human Feedback DemonstrationsPairwise PreferencePairwise Preference
Evaluation Modes Human Eval, Llm As JudgeLlm As JudgeLlm As Judge, Automatic Metrics
Benchmarks WebArena, ToolBenchIf RewardbenchNot reported
Metrics Precision, Pass@1Not reportedAccuracy, Coherence
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit TrajectoryPairwisePairwise
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. HyperMem: Hypergraph Memory for Long-Term Conversations

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Focus: accuracy. Abstract: However, existing approaches as Retrieval-Augmented Generation (RAG) and graph-based memory mostly.

  2. Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: accuracy. Abstract: We introduce a multi-category, cross-cultural benchmark for this task and evaluate VLMs using an.

  3. Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge + pairwise preferences. Abstract: Large language models (LLMs) are increasingly used as automated evaluators (LLM-as-a-Judge).

  4. AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + demonstration data. Focus: WebArena / precision. Abstract: AgentHER realises this idea through a four-stage.

  5. Beyond the Illusion of Consensus: From Surface Heuristics to Knowledge-Grounded Evaluation in LLM-as-a-Judge

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge + rubric ratings. Focus: spearman. Abstract: The paradigm of LLM-as-a-judge relies on a critical assumption, namely.

  6. Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants

    Adds LLM-as-judge with pairwise preferences for broader protocol coverage within this hub. Signals: LLM-as-judge + pairwise preferences. Abstract: Grocery shopping further amplifies these difficulties, as user requests are.

  7. IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation

    Adds LLM-as-judge with pairwise preferences for broader protocol coverage within this hub. Signals: LLM-as-judge + pairwise preferences. Focus: if-rewardbench. Abstract: Instruction-following is a foundational capability of large language.

  8. InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

    Adds LLM-as-judge for broader protocol coverage within this hub. Signals: LLM-as-judge. Focus: Innoeval. Abstract: However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened evaluation dimensions,.

Known Limitations

Known Limitations

  • Only 3.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (13.8% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Pairwise Preference (9)
  • Rubric Rating (4)
  • Critique Edit (2)
  • Demonstrations (1)

Evaluation Modes

  • Llm As Judge (29)
  • Automatic Metrics (11)
  • Simulation Env (6)
  • Human Eval (1)

Top Benchmarks

  • ALFWorld (1)
  • DROP (1)
  • Emobench (1)
  • Eq Bench (1)

Top Metrics

  • Accuracy (7)
  • Coherence (3)
  • Cost (3)
  • F1 (3)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 44.8% · benchmarks 31.0% · metrics 55.2% · quality controls 3.4%.

Top Papers

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