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HFEPX Hub

Llm As Judge + General (Last 30 Days)

Updated from current HFEPX corpus (Apr 19, 2026). 10 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 19, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Llm As Judge, Automatic Metrics. Common annotation unit: Trajectory. Frequently cited benchmark: DROP. 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: 10 Last published: Mar 22, 2026 Global RSS Tag RSS
Llm As JudgeGeneralLast 30d

Researcher Quick Triage

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

High-Signal Coverage

100.0%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 3 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 1 papers support judge-vs-human agreement analysis.
  • 0 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

  • 50% of papers report explicit human-feedback signals, led by pairwise preferences.
  • LLM-as-judge appears in 100% of papers in this hub.
  • DROP 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.
  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly unspecified rater pools, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • DROP appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.
  • SQuAD appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 30% of hub papers (3/10); compare with a secondary metric before ranking methods.
  • coherence is reported in 20% of hub papers (2/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (50% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (30% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (80% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (0% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (50% vs 35% target).

Strengths

  • Strong human-feedback signal (50% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 30% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% 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 (DROP vs SQuAD) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and coherence.
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
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 Not Reported 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
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
Reasoning or Rhetoric? An Empirical Analysis of Moral Reasoning Explanations in Large Language Models

Mar 23, 2026

No
Not Reported
Llm As Judge Not Reported Coherence Not Reported
Multi-Agent Dialectical Refinement for Enhanced Argument Classification

Mar 29, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported F1 , F1 macro Not Reported
Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images

Apr 8, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported Accuracy , Exact match 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… HyperMem: Hypergraph Memory for Long-Term Conversat… Prompt Attack Detection with LLM-as-a-Judge and Mix…
Human Feedback DemonstrationsPairwise PreferenceRed Team
Evaluation Modes Human Eval, Llm As JudgeLlm As Judge, Automatic MetricsLlm As Judge
Benchmarks WebArena, ToolBenchNot reportedNot reported
Metrics Precision, Pass@1Accuracy, CoherenceNot reported
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit TrajectoryPairwiseUnknown
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. Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models

    Adds LLM-as-judge with red-team protocols for broader protocol coverage within this hub. Signals: LLM-as-judge + red-team protocols. Focus: latency. Abstract: In production, guardrails must mitigate these attacks under.

  6. EvoIdeator: Evolving Scientific Ideas through Checklist-Grounded Reinforcement Learning

    Adds LLM-as-judge with rubric ratings for broader protocol coverage within this hub. Signals: LLM-as-judge + rubric ratings. Abstract: Existing Reinforcement Learning (RL) paradigms often rely on rubric-based scalar.

  7. Reasoning or Rhetoric? An Empirical Analysis of Moral Reasoning Explanations in Large Language Models

    Adds LLM-as-judge for broader protocol coverage within this hub. Signals: LLM-as-judge. Focus: coherence. Abstract: Using an LLM-as-judge scoring pipeline validated across three judge models, we classify more than.

  8. LLM-as-a-Judge for Time Series Explanations

    Adds LLM-as-judge for broader protocol coverage within this hub. Signals: LLM-as-judge. Focus: DROP / accuracy. Abstract: Evaluating factual correctness of LLM generated natural language explanations grounded in time.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% 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 (2)
  • Critique Edit (1)
  • Demonstrations (1)
  • Red Team (1)

Evaluation Modes

  • Llm As Judge (10)
  • Automatic Metrics (5)
  • Human Eval (1)
  • Simulation Env (1)

Top Benchmarks

  • DROP (1)
  • SQuAD (1)
  • ToolBench (1)
  • WebArena (1)

Top Metrics

  • Accuracy (3)
  • Coherence (2)
  • F1 (2)
  • Latency (2)

Rater Population Mix

Quality Controls

Coverage diagnostics (sample-based): human-feedback 50.0% · benchmarks 30.0% · metrics 80.0% · quality controls 0.0%.

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…

  • HyperMem: Hypergraph Memory for Long-Term Conversations

    Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang · Apr 9, 2026 · Citations: 0

    Pairwise Preference Llm As JudgeAutomatic Metrics

    Long-term memory is essential for conversational agents to maintain coherence, track persistent tasks, and provide personalized interactions across extended dialogues.

  • Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models

    Hieu Xuan Le, Benjamin Goh, Quy Anh Tang · Mar 26, 2026 · Citations: 0

    Red Team Llm As Judge

    In production, guardrails must mitigate these attacks under strict low-latency constraints, resulting in a deployment gap in which lightweight classifiers and rule-based systems struggle to generalize under distribution shift, while…

  • EvoIdeator: Evolving Scientific Ideas through Checklist-Grounded Reinforcement Learning

    Andreas Sauter, Yuyue Zhao, Jacopo Urbani, Wenxiang Hu, Zaiqiao Meng · Mar 23, 2026 · Citations: 0

    Rubric RatingCritique Edit Llm As Judge

    EvoIdeator leverages a structured judge model to generate two synergistic signals: (1) lexicographic rewards for multi-dimensional optimization, and (2) fine-grained language feedback that offers span-level critiques regarding grounding,…

  • 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…

  • Reasoning or Rhetoric? An Empirical Analysis of Moral Reasoning Explanations in Large Language Models

    Aryan Kasat, Smriti Singh, Aman Chadha, Vinija Jain · Mar 23, 2026 · Citations: 0

    Llm As Judge Long Horizon

    Using an LLM-as-judge scoring pipeline validated across three judge models, we classify more than 600 responses from 13 LLMs spanning a range of architectures, parameter scales, and training regimes across six classical moral dilemmas, and…

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

    Xin Sun, Di Wu, Sijing Qin, Isao Echizen, Abdallah El Ali · Apr 7, 2026 · Citations: 0

    Pairwise Preference Llm As Judge

    Large language models (LLMs) are increasingly used as automated evaluators (LLM-as-a-Judge).

  • Multi-Agent Dialectical Refinement for Enhanced Argument Classification

    Jakub Bąba, Jarosław A. Chudziak · Mar 29, 2026 · Citations: 0

    Llm As JudgeAutomatic Metrics Multi Agent

    We introduce MAD-ACC (Multi-Agent Debate for Argument Component Classification), a framework that leverages dialectical refinement to resolve classification uncertainty.

  • 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…

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

    Yuechen Jiang, Enze Zhang, Md Mohsinul Kabir, Qianqian Xie, Stavroula Golfomitsou · Apr 8, 2026 · Citations: 0

    Llm As JudgeAutomatic Metrics

    We introduce a multi-category, cross-cultural benchmark for this task and evaluate VLMs using an LLM-as-Judge framework that measures semantic alignment with reference annotations.

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