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Human Feedback and Eval Paper Explorer

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Total papers: 4 Search mode: keyword Shortlist (0) RSS

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CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks

Hao Wang, Licheng Pan, Zhichao Chen, Chunyuan Zheng, Zhixuan Chu, Xiaoxi Li · Mar 19, 2026

Citations: 0

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

Score: 90% High protocol signal Freshness: Hot Status: Ready
Pairwise Preference Automatic Metrics Coding
  • Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models, current reward modeling heavily relies on experimental feedback data collected from human annotators under controlled and costly…
  • Extensive experiments across diverse LLM backbones and benchmark datasets validate that CausalRM effectively learns accurate reward signals from noisy and biased observational feedback and delivers substantial performance improvements on…
Open paper
Citations: 0

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

Score: 83% High protocol signal Freshness: Warm Status: Ready
Red Team Simulation Env Coding
  • Large language model (LLM) safety evaluation is moving from content moderation to action security as modern systems gain persistent state, tool access, and autonomous control loops.
  • We present AJAR, a red-teaming framework that exposes multi-turn jailbreak algorithms as callable MCP services and lets an Auditor Agent orchestrate them inside a tool-aware runtime built on Petri.
Open paper
SocialHarmBench: Revealing LLM Vulnerabilities to Socially Harmful Requests

Punya Syon Pandey, Hai Son Le, Devansh Bhardwaj, Rada Mihalcea, Zhijing Jin · Oct 6, 2025

Citations: 0

Match reason: Title directly matches "Harmbench".

Score: 75% Moderate protocol signal Freshness: Cold Status: Fallback
Critique Edit General
  • Yet, existing safety benchmarks rarely test vulnerabilities in domains such as political manipulation, propaganda and disinformation generation, or surveillance and information control.
  • Our evaluations reveal several shortcomings: open-weight models exhibit high vulnerability to harmful compliance, with Mistral-7B reaching attack success rates as high as 97% to 98% in domains such as historical revisionism, propaganda, and…
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