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HFEPX Hub

Llm As Judge + General (Last 90 Days)

Updated from current HFEPX corpus (Apr 18, 2026). 31 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 18, 2026). 31 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: 31 Last published: Mar 22, 2026 Global RSS Tag RSS
Llm As JudgeGeneralLast 90d

Researcher Quick Triage

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

All Sampled Papers (31) Replication-Ready Only (5)

High-Signal Coverage

100.0%

31 / 31 sampled papers are not low-signal flagged.

Replication-Ready Set

5

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

2

Papers containing both `human_eval` and `llm_as_judge`.

  • 5 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 2 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.

Currently showing only replication-ready papers in ranking and matrix sections (5 papers).

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Why This Matters For Eval Research

  • 45.2% 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 (3.2% 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 3.2% of hub papers (1/31); use this cohort for benchmark-matched comparisons.
  • DROP appears in 3.2% of hub papers (1/31); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 22.6% of hub papers (7/31); compare with a secondary metric before ranking methods.
  • cost is reported in 12.9% of hub papers (4/31); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (45.2% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (3.2% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (32.3% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (58.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (12.9% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (41.9% vs 35% target).

Strengths

  • Strong human-feedback signal (45.2% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 29% of papers.

Known Gaps

  • Only 3.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.9% 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 cost.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: ALFWorld Metric Slice: accuracy Recent High-Signal Papers

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
Demonstrations
Human Eval , Llm As Judge WebArena , ToolBench Precision , Pass@1 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 Not Reported
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

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… LLM-as-a-Judge for Time Series Explanations Weakly Supervised Distillation of Hallucination Sig…
Human Feedback DemonstrationsNot reportedNot reported
Evaluation Modes Human Eval, Llm As JudgeLlm As Judge, Automatic MetricsLlm As Judge, Automatic Metrics
Benchmarks WebArena, ToolBenchDROPSQuAD
Metrics Precision, Pass@1Accuracy, FaithfulnessF1
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit TrajectoryRankingUnknown
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 3.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (12.9% 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)
  • Red Team (2)

Evaluation Modes

  • Llm As Judge (31)
  • Automatic Metrics (11)
  • Simulation Env (6)
  • Human Eval (2)

Top Benchmarks

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

Top Metrics

  • Accuracy (7)
  • Cost (4)
  • Coherence (3)
  • F1 (3)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 45.2% · benchmarks 32.3% · metrics 54.8% · quality controls 3.2%.

Top Papers

  • AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

    Liang Ding · Mar 22, 2026 · Citations: 0

    Demonstrations Human EvalLlm As Judge Long Horizon

    LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…

  • LLM-as-a-Judge for Time Series Explanations

    Preetham Sivalingam, Murari Mandal, Saurabh Deshpande, Dhruv Kumar · Apr 2, 2026 · Citations: 0

    Llm As JudgeAutomatic Metrics

    Although modern models generate textual interpretations of numerical signals, existing evaluation methods are limited: reference based similarity metrics and consistency checking models require ground truth explanations, while traditional…

  • Weakly Supervised Distillation of Hallucination Signals into Transformer Representations

    Shoaib Sadiq Salehmohamed, Jinal Prashant Thakkar, Hansika Aredla, Shaik Mohammed Omar, Shalmali Ayachit · Apr 7, 2026 · Citations: 0

    Llm As JudgeAutomatic Metrics

    We introduce a weak supervision framework that combines three complementary grounding signals: substring matching, sentence embedding similarity, and an LLM as a judge verdict to label generated responses as grounded or hallucinated without…

  • Reward Prediction with Factorized World States

    Yijun Shen, Delong Chen, Xianming Hu, Jiaming Mi, Hongbo Zhao · Mar 10, 2026 · Citations: 0

    Llm As JudgeSimulation Env

    We evaluate on RewardPrediction, a new benchmark dataset spanning five diverse domains and comprising 2,454 unique action-observation trajectories with step-wise ground-truth rewards.

  • ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts

    Trapoom Ukarapol, Nut Chukamphaeng, Kunat Pipatanakul, Pakhapoom Sarapat · Mar 5, 2026 · Citations: 0

    Llm As JudgeAutomatic Metrics

    Using ThaiSafetyBench, we evaluate 24 LLMs, with GPT-4.1 and Gemini-2.5-Pro serving as LLM-as-a-judge evaluators.

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