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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: 91 Search mode: keyword RSS

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LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering

Rafid Ishrak Jahan, Fahmid Shahriar Iqbal, Sagnik Ray Choudhury · Feb 27, 2026

Citations: 0
Pairwise PreferenceRubric Rating General
  • We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA.
  • We propose nine rubrics for answer quality evaluation, and show that simple linear models based on these features perform comparably to state-of-the-art LLM evaluators.
Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks

Kunihiro Miyazaki, Takanobu Kawahara, Stephen Roberts, Stefan Zohren · Feb 26, 2026

Citations: 0
Pairwise Preference Multi Agent General
  • While mainstream approaches deploy multi-agent systems mimicking analyst and manager roles, they often rely on abstract instructions that overlook the intricacies of real-world workflows, which can lead to degraded inference performance and…
  • Therefore, we propose a multi-agent LLM trading framework that explicitly decomposes investment analysis into fine-grained tasks, rather than providing coarse-grained instructions.
Citations: 0
Pairwise Preference General
  • Inspired by Humphrey's ipsundrum hypothesis, we implement ReCoN-Ipsundrum, an inspectable agent that extends a ReCoN state machine with a recurrent persistence loop over sensory salience Ns and an optional affect proxy reporting…
  • Across fixed-parameter ablations (ReCoN, Ipsundrum, Ipsundrum+affect), we operationalize Humphrey's qualiaphilia (preference for sensory experience for its own sake) as a familiarity-controlled scenic-over-dull route choice.
Decentralized Ranking Aggregation: Gossip Algorithms for Borda and Copeland Consensus

Anna Van Elst, Kerrian Le Caillec, Igor Colin, Stephan Clémençon · Feb 26, 2026

Citations: 0
Pairwise Preference Multi Agent General
  • The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originating in social choice theory, have been documented in the literature, offering theoretical…
  • peer-to-peer networks, IoT, multi-agent systems), extending the ability to calculate consensus rankings with guarantees in a decentralized setting, i.e., when preference data is initially distributed across a communicating network, remains…
Moral Preferences of LLMs Under Directed Contextual Influence

Phil Blandfort, Tushar Karayil, Urja Pawar, Robert Graham, Alex McKenzie, Dmitrii Krasheninnikov · Feb 26, 2026

Citations: 0
Pairwise Preference General
  • Moral benchmarks for LLMs typically use context-free prompts, implicitly assuming stable preferences.
  • We introduce a pilot evaluation harness for directed contextual influence in trolley-problem-style moral triage: for each demographic factor, we apply matched, direction-flipped contextual influences that differ only in which group they…
RLHFless: Serverless Computing for Efficient RLHF

Rui Wei, Hanfei Yu, Shubham Jain, Yogarajan Sivakumar, Devesh Tiwari, Jian Li · Feb 26, 2026

Citations: 0
Pairwise Preference Automatic Metrics General
  • Reinforcement Learning from Human Feedback (RLHF) has been widely applied to Large Language Model (LLM) post-training to align model outputs with human preferences.
Same Words, Different Judgments: Modality Effects on Preference Alignment

Aaron Broukhim, Nadir Weibel, Eshin Jolly · Feb 26, 2026

Citations: 0
Pairwise PreferenceRlaif Or Synthetic Feedback Automatic Metrics General
  • Preference-based reinforcement learning (PbRL) is the dominant framework for aligning AI systems to human preferences, but its application to speech remains underexplored.
  • We present a controlled cross-modal study of human and synthetic preference annotations, comparing text and audio evaluations of identical semantic content across 100 prompts.
Citations: 0
Pairwise Preference Automatic Metrics General
  • Although initially formulated for human truth-telling under asymmetric stakes, the same phase-dynamic architecture extends to AI systems operating under policy constraints and alignment filters.
  • The framework therefore provides a unified structural account of both human silence under pressure and AI preference-driven distortion.
CCCaption: Dual-Reward Reinforcement Learning for Complete and Correct Image Captioning

Zhijiang Tang, Linhua Wang, Jiaxin Qi, Weihao Jiang, Peng Hou, Anxiang Zeng · Feb 25, 2026

Citations: 0
Pairwise Preference Automatic Metrics General
  • Image captioning remains a fundamental task for vision language understanding, yet ground-truth supervision still relies predominantly on human-annotated references.
  • Because human annotations reflect subjective preferences and expertise, ground-truth captions are often incomplete or even incorrect, which in turn limits caption models.
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences

Sweta Karlekar, Carolina Zheng, Magnus Saebo, Nicolas Beltran-Velez, Shuyang Yu, John Bowlan · Feb 25, 2026

Citations: 0
Pairwise Preference Automatic Metrics Math
  • Building on this observation, we introduce Duel-Evolve, an evolutionary optimization algorithm that replaces external scalar rewards with pairwise preferences elicited from the same LLM used to generate candidates.
  • Results show that pairwise self-preferences provide strong optimization signal for test-time improvement over large, discrete output spaces.
Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment

Mengxuan Hu, Vivek V. Datla, Anoop Kumar, Zihan Guan, Sheng Li, Alfy Samuel · Feb 24, 2026

Citations: 0
Pairwise PreferenceRed Team General
  • Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have improved the safety of large language models (LLMs).
  • Furthermore, inspired by failure patterns in CoT fine-tuning, we introduce Alignment-Weighted DPO, which targets the most problematic parts of an output by assigning different preference weights to the reasoning and final-answer segments.
Probing Graph Neural Network Activation Patterns Through Graph Topology

Floriano Tori, Lorenzo Bini, Marco Sorbi, Stéphane Marchand-Maillet, Vincent Ginis · Feb 24, 2026

Citations: 0
Pairwise Preference Automatic Metrics General
  • However, it remains unclear how the topology of a graph interacts with the learned preferences of GNNs.
  • Our findings on synthetic graphs and molecular benchmarks reveal that MAs do not preferentially concentrate on curvature extremes, despite their theoretical link to information flow.
HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders

Kun Yuan, Junyu Bi, Daixuan Cheng, Changfa Wu, Shuwen Xiao, Binbin Cao · Feb 24, 2026

Citations: 0
Pairwise Preference General
  • Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory constraints.
  • While summarizing history via interest centers offers a practical alternative, existing methods struggle to (1) identify user-specific centers at appropriate granularity and (2) accurately assign behaviors, leading to quantization errors…
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.
CAMEL: Confidence-Gated Reflection for Reward Modeling

Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar, Kun Xu · Feb 24, 2026

Citations: 0
Pairwise PreferenceCritique Edit Automatic Metrics General
  • Building on this insight, we propose CAMEL, a confidence-gated reflection framework that performs a lightweight single-token preference decision first and selectively invokes reflection only for low-confidence instances.
  • Empirically, CAMEL achieves state-of-the-art performance on three widely used reward-model benchmarks with 82.9% average accuracy, surpassing the best prior model by 3.2% and outperforming 70B-parameter models using only 14B parameters,…
gencat: Generative computerized adaptive testing

Wanyong Feng, Andrew Lan · Feb 23, 2026

Citations: 0
Pairwise Preference Coding
  • We train the model in a two-step process, first via Supervised Fine-Tuning and then via preference optimization for knowledge-response alignment.
Citations: 0
Pairwise Preference Long Horizon General
  • Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context.
  • Extensive experiments on the LaMP-QA benchmark using three LLMs show that PR2 consistently outperforms strong baselines, achieving an average relative improvement of 8.8%-12% in personalized QA.
Yor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language

Toheeb Aduramomi Jimoh, Tabea De Wille, Nikola S. Nikolov · Feb 21, 2026

Citations: 0
Pairwise Preference Automatic Metrics General
  • This protocol incorporates context-sensitive interpretation and community-informed guidelines and is accompanied by a comprehensive analysis of inter-annotator agreement to support replication in other African languages.
  • One annotator pair achieved almost perfect agreement (κ= 0.8743; 93.8\% raw agreement), exceeding a number of reported benchmarks for English sarcasm research works.

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