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HFEPX Hub

Llm As Judge Or Simulation Env Papers

Updated from current HFEPX corpus (Feb 27, 2026). 123 papers are grouped in this hub page. Common evaluation modes: Simulation Env, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Inter Annotator Agreement Reported. Frequently cited benchmark: Retrieval. 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 Feb 26, 2026.

Papers: 123 Last published: Feb 26, 2026 Global RSS Tag RSS
Llm As JudgeSimulation Env

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 123 papers for Llm As Judge Or Simulation Env Papers. Dominant protocol signals include simulation environments, automatic metrics, LLM-as-judge, with frequent benchmark focus on Retrieval, APPS and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 7.3% of hub papers (9/123); use this cohort for benchmark-matched comparisons.
  • APPS appears in 1.6% of hub papers (2/123); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 15.4% of hub papers (19/123); compare with a secondary metric before ranking methods.
  • cost is reported in 9.8% of hub papers (12/123); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (14.6% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (3.3% vs 30% target).
  • Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (24.4% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (48% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (9.8% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (13.8% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (14.6% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (3.3% vs 30% target).

Papers naming benchmarks/datasets

Coverage is usable but incomplete (24.4% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (48% vs 35% target).

Papers with known rater population

Coverage is a replication risk (9.8% vs 35% target).

Papers with known annotation unit

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

Suggested Reading Order

  1. 1. CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification

    Include a human-eval paper to anchor calibration against automated judge settings.

  5. 5. Dynamic Personality Adaptation in Large Language Models via State Machines

    Adds simulation environments for broader coverage within this hub.

  6. 6. TG-ASR: Translation-Guided Learning with Parallel Gated Cross Attention for Low-Resource Automatic Speech Recognition

    Adds simulation environments for broader coverage within this hub.

  7. 7. Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Scalable Kernel-Based Distances for Statistical Inference and Integration

    Adds simulation environments for broader coverage within this hub.

Known Limitations

  • Only 3.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.8% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

human_eval vs llm_as_judge

both=2, left_only=3, right_only=8

2 papers use both Human Eval and Llm As Judge.

human_eval vs automatic_metrics

both=1, left_only=4, right_only=21

1 papers use both Human Eval and Automatic Metrics.

llm_as_judge vs automatic_metrics

both=2, left_only=8, right_only=20

2 papers use both Llm As Judge and Automatic Metrics.

Benchmark Brief

Retrieval

Coverage: 9 papers (7.3%)

9 papers (7.3%) mention Retrieval.

Examples: Retrieval Challenges in Low-Resource Public Service Information: A Case Study on Food Pantry Access , InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation , Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents

Benchmark Brief

APPS

Coverage: 2 papers (1.6%)

2 papers (1.6%) mention APPS.

Examples: UI-Venus-1.5 Technical Report , The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution

Benchmark Brief

BrowseComp

Coverage: 2 papers (1.6%)

2 papers (1.6%) mention BrowseComp.

Examples: BrowseComp-$V^3$: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents , Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

Metric Brief

accuracy

Coverage: 19 papers (15.4%)

19 papers (15.4%) mention accuracy.

Examples: Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text , MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification , Overton Pluralistic Reinforcement Learning for Large Language Models

Metric Brief

cost

Coverage: 12 papers (9.8%)

12 papers (9.8%) mention cost.

Examples: Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning , Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents , MAEB: Massive Audio Embedding Benchmark

Metric Brief

coherence

Coverage: 7 papers (5.7%)

7 papers (5.7%) mention coherence.

Examples: ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning , Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning , From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery , TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation , Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery , TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation , Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery , TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation , Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction

Top Papers

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