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HFEPX Hub

Automatic Metrics + General + Llm As Judge Papers

Updated from current HFEPX corpus (Apr 27, 2026). 18 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 18 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Calibration. 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 Apr 9, 2026.

Papers: 18 Last published: Apr 9, 2026 Global RSS Tag RSS
Automatic MetricsGeneralLlm As Judge

Researcher Quick Triage

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

All Sampled Papers (18) Replication-Ready Only (3)

High-Signal Coverage

100.0%

18 / 18 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 3 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 2 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 (3 papers).

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Why This Matters For Eval Research

  • 5.6% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% of papers in this hub.
  • DROP is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (11.1% of papers).
  • Rater context is mostly domain experts, and annotation is commonly pairwise annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • DROP appears in 5.6% of hub papers (1/18); use this cohort for benchmark-matched comparisons.
  • SQuAD appears in 5.6% of hub papers (1/18); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 66.7% of hub papers (12/18); compare with a secondary metric before ranking methods.
  • f1 is reported in 27.8% of hub papers (5/18); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (5.6% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (11.1% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (16.7% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (100% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (5.6% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (16.7% vs 35% target).

Strengths

  • Agentic evaluation appears in 27.8% of papers.

Known Gaps

  • Only 11.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (5.6% coverage).
  • Annotation unit is under-specified (16.7% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (DROP vs SQuAD) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and f1.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

LLM-as-Judge Protocols Benchmark Slice: DROP 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
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
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 LLM-as-a-Judge for Time Series Explanations Weakly Supervised Distillation of Hallucination Sig… ThaiSafetyBench: Assessing Language Model Safety in…
Human Feedback Not reportedNot reportedNot reported
Evaluation Modes Llm As Judge, Automatic MetricsLlm As Judge, Automatic MetricsLlm As Judge, Automatic Metrics
Benchmarks DROPSQuADThaisafetybench
Metrics Accuracy, FaithfulnessF1F1, F1 weighted
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit RankingUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: accuracy. Abstract: Under a five-dimensional LLM-as-Judge evaluation protocol, the overall average score increases by approximately 28%.

  2. 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.

  3. 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.

  4. Mind the Shift: Decoding Monetary Policy Stance from FOMC Statements with Large Language Models

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge. Focus: accuracy. Abstract: We introduce Delta-Consistent Scoring (DCS), an annotation-free framework that maps frozen large language.

  5. 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.

  6. Multi-Agent Dialectical Refinement for Enhanced Argument Classification

    Adds LLM-as-judge for broader protocol coverage within this hub. Signals: LLM-as-judge. Focus: f1. Abstract: MAD-ACC utilizes a Proponent-Opponent-Judge model where agents defend conflicting interpretations of ambiguous text, exposing.

  7. The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI

    Adds LLM-as-judge for broader protocol coverage within this hub. Signals: LLM-as-judge. Focus: accuracy. Abstract: As Large Language Models (LLMs) transition from standalone chat interfaces to foundational reasoning layers.

  8. Multi-Agent LLMs for Generating Research Limitations

    Adds LLM-as-judge for broader protocol coverage within this hub. Signals: LLM-as-judge. Focus: bleu. Abstract: A Judge agent refines their outputs, and a Master agent consolidates them into a.

Known Limitations

Known Limitations

  • Only 11.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (5.6% 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 (1)

Evaluation Modes

  • Automatic Metrics (18)
  • Llm As Judge (18)

Top Benchmarks

  • DROP (1)
  • SQuAD (1)
  • Thaisafetybench (1)

Top Metrics

  • Accuracy (12)
  • F1 (5)
  • Cost (3)
  • Coherence (2)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

  • Calibration (2)
Coverage diagnostics (sample-based): human-feedback 5.6% · benchmarks 16.7% · metrics 100.0% · quality controls 11.1%.

Top Papers

  • 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…

  • 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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