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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: 30 Search mode: keyword RSS

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The Trinity of Consistency as a Defining Principle for General World Models

Jingxuan Wei, Siyuan Li, Yuhang Xu, Zheng Sun, Junjie Jiang, Hexuan Jin · Feb 26, 2026

Citations: 0
Simulation Env Long Horizon Law
  • To complement this conceptual framework, we introduce CoW-Bench, a benchmark centered on multi-frame reasoning and generation scenarios.
  • CoW-Bench evaluates both video generation models and UMMs under a unified evaluation protocol.
Distill and Align Decomposition for Enhanced Claim Verification

Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero, Arturo Oncevay · Feb 25, 2026

Citations: 0
Human EvalAutomatic Metrics General
  • Across six evaluation settings, our trained 8B decomposer improves downstream verification performance to (71.75%) macro-F1, outperforming prompt-based approaches ((+1.99), (+6.24)) and existing RL methods ((+5.84)).
  • Human evaluation confirms the high quality of the generated subclaims.
Citations: 0
Pairwise PreferenceRlaif Or Synthetic Feedback Human Eval General
  • Preference-based RL offers an appealing alternative by learning from human preferences over pairs of behavioural outcomes.
  • More recently, RL from AI feedback (RLAIF) has demonstrated that large language models (LLMs) can generate preference labels at scale, mitigating the reliance on human annotators.
Citations: 0
Human EvalAutomatic Metrics General
  • Experiments show that CARE outperforms leading LLMs and substantially reduces the gap between counselor evaluations and client-perceived alliance, achieving over 70% higher Pearson correlation with client ratings.
  • CARE also produces high-quality, contextually grounded rationales, validated by both automatic and human evaluations.
Citations: 0
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.
Citations: 0
Automatic Metrics Multi Agent Law
  • We introduce Whisper: Courtside Edition, a novel multi-agent large language model (LLM) pipeline that enhances Whisper transcriptions without retraining.
  • The pipeline intercepts Whisper's initial transcript, applies specialized LLM agents for domain context identification, named entity recognition, and jargon detection, and generates compact prompts that guide Whisper's decoder.
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

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

Citations: 0
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.
Validating Political Position Predictions of Arguments

Jordan Robinson, Angus R. Williams, Katie Atkinson, Anthony G. Cohn · Feb 20, 2026

Citations: 0
Pairwise Preference Human Eval General
  • Real-world knowledge representation often requires capturing subjective, continuous attributes -- such as political positions -- that conflict with pairwise validation, the widely accepted gold standard for human evaluation.
  • We address this challenge through a dual-scale validation framework applied to political stance prediction in argumentative discourse, combining pointwise and pairwise human annotation.
Citations: 0
Human EvalAutomatic Metrics Law
  • Vichara surpasses existing judgment prediction benchmarks on both datasets, with GPT-4o mini achieving the highest performance (F1: 81.5 on PredEx, 80.3 on ILDC_expert), followed by Llama-3.1-8B.
  • Human evaluation of the generated explanations across Clarity, Linking, and Usefulness metrics highlights GPT-4o mini's superior interpretability.
Claim Automation using Large Language Model

Zhengda Mo, Zhiyu Quan, Eli O'Donohue, Kaiwen Zhong · Feb 18, 2026

Citations: 0
Human EvalAutomatic Metrics General
  • We assess this module using a multi-dimensional evaluation framework that combines automated semantic similarity metrics with human evaluation, enabling a rigorous examination of both practical utility and predictive accuracy.
Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents

Nivya Talokar, Ayush K Tarun, Murari Mandal, Maksym Andriushchenko, Antoine Bosselut · Feb 18, 2026

Citations: 0
Red Team LawMultilingual
  • LLM-based agents execute real-world workflows via tools and memory.
  • We introduce STING (Sequential Testing of Illicit N-step Goal execution), an automated red-teaming framework that constructs a step-by-step illicit plan grounded in a benign persona and iteratively probes a target agent with adaptive…
Discovering Implicit Large Language Model Alignment Objectives

Edward Chen, Sanmi Koyejo, Carlos Guestrin · Feb 17, 2026

Citations: 0
Rubric Rating Human Eval General
  • To address these limitations, we introduce Obj-Disco, a framework that automatically decomposes an alignment reward signal into a sparse, weighted combination of human-interpretable natural language objectives.
  • Experiments with popular open-source reward models show that the framework consistently captures > 90% of reward behavior, a finding further corroborated by human evaluation.
Citations: 0
Human EvalSimulation Env Long Horizon Coding
  • Within this framework, we construct framing-sensitive agent personas by fine-tuning language models with framing-conditioned loss attenuation, inducing targeted biases while preserving overall task competence.
  • Human evaluation further confirms that FrameRef's generated framings measurably affect human judgment.
HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Weiqi Zhai, Zhihai Wang, Jinghang Wang, Boyu Yang, Xiaogang Li, Xander Xu · Feb 15, 2026

Citations: 0
Expert VerificationCritique Edit Automatic Metrics Law
  • Humanity's Last Exam (HLE) has become a widely used benchmark for evaluating frontier large language models on challenging, multi-domain questions.
  • However, community-led analyses have raised concerns that HLE contains a non-trivial number of noisy items, which can bias evaluation results and distort cross-model comparisons.
The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage

Preni Golazizian, Elnaz Rahmati, Jackson Trager, Zhivar Sourati, Nona Ghazizadeh, Georgios Chochlakis · Feb 10, 2026

Citations: 0
Pairwise PreferenceRubric Rating Law
  • By sampling annotators from police-affiliated, justice-system-impacted, and non-affiliated Los Angeles residents, we enable the systematic study of perceptual differences across diverse communities.
  • To this end, we (i) develop a domain-specific evaluation rubric grounded in procedural justice theory, LAPD training materials, and extensive fieldwork; (ii) introduce a rubric-driven preference data construction framework for…
Citations: 0
Pairwise PreferenceCritique Edit Human Eval General
  • In this paper, we introduce RebuttalAgent, the first framework to ground academic rebuttal in Theory of Mind (ToM), operationalized through a ToM-Strategy-Response (TSR) framework that models reviewer mental state, formulates persuasion…
  • Extensive experiments show RebuttalAgent significantly outperforms the base model by an average of 18.3% on automated metrics, while also outperforming advanced proprietary models across both automated and human evaluations.
APEX-Agents

Bertie Vidgen, Austin Mann, Abby Fennelly, John Wright Stanly, Lucas Rothman, Marco Burstein · Jan 20, 2026

Citations: 0
Rubric RatingExpert Verification Automatic Metrics Long Horizon Law
  • We introduce the AI Productivity Index for Agents (APEX-Agents), a benchmark for assessing whether AI agents can execute long-horizon, cross-application tasks created by investment banking analysts, management consultants, and corporate…
  • We test eight agents for the leaderboard using Pass@1.
Multimodal Multi-Agent Empowered Legal Judgment Prediction

Zhaolu Kang, Junhao Gong, Qingxi Chen, Hao Zhang, Jiaxin Liu, Rong Fu · Jan 19, 2026

Citations: 0
Simulation Env Multi Agent Law
  • Furthermore, we build JurisMM, a large dataset with over 100,000 recent Chinese judicial records, including both text and multimodal video-text data, enabling comprehensive evaluation.
  • Experiments on JurisMM and the benchmark LawBench validate our framework's effectiveness.
HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue

Laya Iyer, Kriti Aggarwal, Sanmi Koyejo, Gail Heyman, Desmond C. Ong, Subhabrata Mukherjee · Jan 9, 2026

Citations: 0
Pairwise PreferenceRubric Rating Human EvalLlm As Judge General
  • Despite rapid progress in language models, we still lack a clear way to understand how their abilities in these interpersonal domains compare to those of humans.
  • We introduce HEART, the first-ever framework that directly compares humans and LLMs on the same multi-turn emotional-support conversations.

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