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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: 8 Search mode: keyword Shortlist (0) RSS

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Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE

Hejin Huang, Jusheng Zhang, Kaitong Cai, Jian Wang, Rong Pan · Mar 31, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 90% Moderate protocol signal Freshness: Hot Status: Ready
Pairwise Preference Automatic Metrics General
  • Preference-based alignment objectives have been widely adopted, from RLHF-style pairwise learning in large language models to emerging applications in recommender systems.
  • With an optional sparse Mixture-of-Experts encoder for efficient capacity scaling, RoDPO achieves up to 5.25% NDCG@5 on three Amazon benchmarks, with nearly unchanged inference cost.
Open paper
AgentDrift: Unsafe Recommendation Drift Under Tool Corruption Hidden by Ranking Metrics in LLM Agents

Zekun Wu, Adriano Koshiyama, Sahan Bulathwela, Maria Perez-Ortiz · Mar 13, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 83% Moderate protocol signal Freshness: Warm Status: Fallback
Automatic Metrics Long Horizon General
  • Tool-augmented LLM agents increasingly operate as multi-turn advisors in high-stakes domains, yet their evaluation relies on ranking metrics that measure what is recommended but not whether it is safe for the user.
  • We observe evaluation blindness: recommendation quality is preserved under contamination (UPR~1.0) while risk-inappropriate products appear in 65-93% of turns, invisible to standard NDCG.
Open paper
PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval

Chun Chet Ng, Jia Yu Lim, Wei Zeng Low · Nov 18, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 78% High protocol signal Freshness: Cold Status: Fallback
Automatic Metrics Multi Agent Coding
  • We present PRISM, a training-free framework that integrates refined system prompting, in-context learning (ICL), and lightweight multi-agent coordination for document and chunk ranking tasks.
  • Our primary contribution is a systematic empirical study of when each component provides value: prompt engineering delivers consistent performance with minimal overhead, ICL enhances reasoning for complex queries when applied selectively,…
Open paper

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