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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 RSS

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Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes

Rahul Garg, Trilok Padhi, Hemang Jain, Ugur Kursuncu, Ponnurangam Kumaraguru · Nov 19, 2024

Citations: 0
Automatic MetricsSimulation Env General
  • Experimental results from our study on two hate speech benchmark datasets demonstrate superior performance over the state-of-the-art baselines across AU-ROC, F1, and Recall with improvements of 1.1%, 7%, and 35%, respectively.
MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

Zexue He, Yu Wang, Churan Zhi, Yuanzhe Hu, Tzu-Ping Chen, Lang Yin · Feb 18, 2026

Citations: 0
Pairwise Preference Simulation Env Web Browsing General
  • Existing evaluations of agents with memory typically assess memorization and action in isolation.
  • One class of benchmarks evaluates memorization by testing recall of past conversations or text but fails to capture how memory is used to guide future decisions.
CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications

Victoria Blake, Mathew Miller, Jamie Novak, Sze-yuan Ooi, Blanca Gallego · Feb 20, 2026

Citations: 0
Expert Verification Automatic Metrics Medicine
  • The framework was evaluated on five lexically heterogeneous clinical concepts against a manually curated benchmark and gold-standard concept sets.
  • Results Across all concepts, CUICurate produced substantially larger and more complete concept sets than the manual benchmarks whilst matching human precision.
Citations: 0
Automatic MetricsSimulation Env General
  • When deterministic scoring cannot resolve an ambiguity, the system escalates to multimodal and constrained large-language-model reasoning, followed by a single human-in-the-loop (HITL) review step.
  • By prioritizing deterministic rules, clear decision tracking, and retaining unresolved cases for human review, the framework provides a practical foundation for downstream manufacturing automation in real-world industrial environments.
An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Cathy Shyr, Yan Hu, Rory J. Tinker, Thomas A. Cassini, Kevin W. Byram, Rizwan Hamid · Feb 23, 2026

Citations: 0
Expert Verification Automatic Metrics Medicine
  • Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not operationalize the full clinical workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontolo
  • Using clinician-curated HPO terms as the gold standard, RARE-PHENIX consistently outperformed a state-of-the-art deep learning baseline (PhenoBERT) across ontology-based similarity and precision-recall-F1 metrics in end-to-end evaluation (i

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