Skip to content

Researcher Tools

Human Feedback and Eval Paper Explorer

A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research. Every paper includes structured metadata for quick triage.

Total papers: 7 Search mode: keyword Shortlist (0) RSS

Featured Papers

Popular high-signal papers with direct links to full protocol pages.

Browse by Topic

Jump directly into tag and hub pages to crawl deeper content clusters.

Popular Tags

Top Protocol Hubs

Weekly Eval Paper Digest

The top RLHF, evaluation, and human feedback papers — curated and summarized every Friday.

No spam. Unsubscribe anytime.

Start Here By Objective

Pick your immediate research objective and jump directly to high-signal pages, not generic search.

Scale Your Evaluation Team

Need human evaluators for your benchmark or preference study? OpenTrain sources pre-vetted domain experts into your annotation pipeline.

LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation

Koki Itai, Shunichi Hasegawa, Yuta Yamamoto, Gouki Minegishi, Masaki Otsuki · Mar 6, 2026

Citations: 0

Match reason: Matches selected tags (Automatic Metrics, Coding).

Score: 58% High protocol signal Freshness: Warm Status: Ready
Llm As JudgeAutomatic Metrics Long Horizon CodingMultilingual
  • To bridge the gap between existing evaluations and practical use, we introduce LIT-RAGBench (the Logic, Integration, Table, Reasoning, and Abstention RAG Generator Benchmark), which defines five categories: Integration, Reasoning, Logic,…
  • We use LLM-as-a-Judge for scoring and report category-wise and overall accuracy.
Open paper

Match reason: Matches selected tags (Automatic Metrics, Coding).

Score: 58% Moderate protocol signal Freshness: Warm Status: Fallback
Human EvalLlm As Judge Coding
  • Gemini also serves as an LLM-as-a-judge system for automatic evaluation in our experiments.
  • The automated judgments were verified through human evaluation, demonstrating high agreement (kappa = 0.87).
Open paper
Citations: 0

Match reason: Matches selected tags (Automatic Metrics, Coding).

Score: 58% Moderate protocol signal Freshness: Warm Status: Fallback
Llm As JudgeAutomatic Metrics Coding
  • We evaluate LLM-as-a-judge marking across three physics assessment formats - structured questions, written essays, and scientific plots - comparing GPT-5.2, Grok 4.1, Claude Opus 4.5, DeepSeek-V3.2, Gemini Pro 3, and committee aggregations…
  • Across n=55 scripts (n=275 essays), blind AI marking is harsher and more variable than human marking, with discriminative validity already poor (ρ\approx 0.1).
Open paper
DeEscalWild: A Real-World Benchmark for Automated De-Escalation Training with SLMs

Md Hasebul Hasan, Krity Haque Charu, Eshwara Prasad Sridhar, Shuchisnigdha Deb, Mohammad A. Islam · Mar 20, 2026

Citations: 0

Match reason: Matches selected tags (Automatic Metrics, Coding).

Score: 55% Moderate protocol signal Freshness: Warm Status: Fallback
Human EvalLlm As Judge LawCoding
  • To bridge this gap, we present DeEscalWild, a novel benchmark dataset curated from a multi-stage pipeline of in-the-wild police-civilian interactions extracted from publicly available video repositories.
  • Extensive experiments demonstrate that SLMs fine-tuned on this data significantly outperform their base counterparts across ROUGE-L, BLEU-4, METEOR, BERTScore, Realism Score, and human evaluation metrics.
Open paper
Automatically Benchmarking LLM Code Agents through Agent-Driven Annotation and Evaluation

Lingyue Fu, Bolun Zhang, Hao Guan, Yaoming Zhu, Lin Qiu, Weiwen Liu · Oct 28, 2025

Citations: 0

Match reason: Matches selected tags (Automatic Metrics, Coding).

Score: 53% Moderate protocol signal Freshness: Cold Status: Ready
Expert Verification Llm As JudgeAutomatic Metrics Coding
  • To address these challenges, we propose an agent-driven benchmark construction pipeline that leverages human supervision to efficiently generate diverse project-level tasks.
  • Furthermore, to overcome the inaccuracy of general LLM judges, we propose a highly reliable evaluation framework powered by a specialized, fine-tuned model.
Open paper
Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation

Yanzhi Tian, Cunxiang Wang, Zeming Liu, Heyan Huang, Wenbo Yu, Dawei Song · Jan 12, 2026

Citations: 0

Match reason: Matches selected tags (Automatic Metrics, Coding).

Score: 52% Sparse protocol signal Freshness: Warm Status: Fallback
Llm As JudgeAutomatic Metrics CodingMultilingual
  • To systematically investigate the reliability of MT metrics, we first curate a meta-evaluation dataset focused on non-literal translations, namely MENT.
  • To mitigate these limitations, we propose RATE, a novel agentic translation evaluation framework, centered by a reflective Core Agent that dynamically invokes specialized sub-agents.
Open paper
DeepPrune: Parallel Scaling without Inter-trace Redundancy

Shangqing Tu, Yaxuan Li, Yushi Bai, Lei Hou, Juanzi Li · Oct 9, 2025

Citations: 0

Match reason: Matches selected tags (Automatic Metrics, Coding).

Score: 53% Moderate protocol signal Freshness: Cold Status: Fallback
Llm As JudgeAutomatic Metrics MathCoding
  • Our method features a specialized judge model trained with out-of-distribution data (AIME 2022, AIME 2023, and MATH 500) using oversampling techniques to accurately predict answer equivalence from partial reasoning traces, achieving 0.7072…
  • Comprehensive evaluations across three challenging benchmarks (AIME 2024, AIME 2025, and GPQA) and multiple reasoning models demonstrate that DeepPrune achieves remarkable token reduction of 65.73%--88.50% compared to conventional consensus…
Open paper

Protocol Hubs

Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.