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: 5 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.

Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

Zhiwen You, Xi Chen, Aniket Vashishtha, Simo Du, Gabriel Erion-Barner, Hongyuan Mei · Mar 29, 2026

Citations: 0

Match reason: Matches selected tags (Medicine, Human Eval).

Score: 58% High protocol signal Freshness: Warm Status: Ready
Expert Verification Human EvalAutomatic Metrics Multi Agent Medicine
  • In this work, we propose a counterfactual multi-agent diagnostic framework inspired by clinician training that makes hypothesis testing explicit and evidence-grounded.
  • Across three diagnostic benchmarks and seven LLMs, our method consistently improves diagnostic accuracy over prompting and prior multi-agent baselines, with the largest gains observed in complex and ambiguous cases.
Open paper
Citations: 0

Match reason: Matches selected tags (Medicine, Human Eval).

Score: 58% Moderate protocol signal Freshness: Warm Status: Ready
Expert Verification Human Eval Medicine
  • To address this gap, we introduce CPGBench, an automated framework benchmarking the clinical guideline detection and adherence capabilities of LLMs in multi-turn conversations.
  • To confirm the validity of our automatic analysis, we further conduct a comprehensive human evaluation involving 56 clinicians from different specialties.
Open paper
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

Abraham Paul Elenjical, Vivek Hruday Kavuri, Vasudeva Varma · Feb 21, 2026

Citations: 0

Match reason: Matches selected tags (Medicine, Human Eval).

Score: 58% Moderate protocol signal Freshness: Warm Status: Ready
Pairwise Preference Human Eval MathMedicine
  • We introduce a psychologically grounded metacognitive framework that operationalizes Ann Brown's regulatory cycle (Planning, Monitoring, and Evaluation) as a structured prompting architecture, and study its integration within a lightweight…
  • Blinded human evaluations over 580 query pairs show an 84% aggregate preference for trustworthiness and metacognitive self-awareness over standard and Chain-of-Thought baselines.
Open paper
Less Is More? Selective Visual Attention to High-Importance Regions for Multimodal Radiology Summarization

Mst. Fahmida Sultana Naznin, Adnan Ibney Faruq, Mushfiqur Rahman, Niloy Kumar Mondal, Md. Mehedi Hasan Shawon, Md Rakibul Hasan · Mar 31, 2026

Citations: 0

Match reason: Matches selected tags (Medicine, Human Eval).

Score: 55% Moderate protocol signal Freshness: Warm Status: Fallback
Human EvalAutomatic Metrics Medicine
  • Through controlled ablations on MIMIC-CXR benchmark, we show that selectively focusing on pathology-relevant visual patches rather than full images yields substantially better performance.
  • ViTAS achieves SOTA results with 29.25% BLEU-4 and 69.83% ROUGE-L, improved factual alignment in qualitative analysis, and the highest expert-rated human evaluation scores.
Open paper
Citations: 0

Match reason: Matches selected tags (Medicine, Human Eval).

Score: 52% Sparse protocol signal Freshness: Warm Status: Fallback
Human EvalLlm As Judge Medicine
  • We present AgenticSum, an inference-time, agentic framework that separates context selection, generation, verification, and targeted correction to reduce hallucinated content.
  • We evaluate AgenticSum on two public datasets, using reference-based metrics, LLM-as-a-judge assessment, and human evaluation.
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.