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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: 23 Search mode: keyword RSS

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Terminology Rarity Predicts Catastrophic Failure in LLM Translation of Low-Resource Ancient Languages: Evidence from Ancient Greek

James L. Zainaldin, Cameron Pattison, Manuela Marai, Jacob Wu, Mark J. Schiefsky · Feb 27, 2026

Citations: 0
Human EvalAutomatic Metrics Multilingual
  • This study presents the first systematic, reference-free human evaluation of large language model (LLM) machine translation (MT) for Ancient Greek (AG) technical prose.
  • We assess translation quality using both standard automated evaluation metrics (BLEU, chrF++, METEOR, ROUGE-L, BERTScore, COMET, BLEURT) and expert human evaluation via a modified Multidimensional Quality Metrics (MQM) framework applied to…
Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

Zhicheng Fang, Jingjie Zheng, Chenxu Fu, Wei Xu · Feb 27, 2026

Citations: 0
Red Team Llm As Judge Multi Agent CodingMultilingual
  • Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols.
  • We introduce JAILBREAK FOUNDRY (JBF), a system that addresses this gap via a multi-agent workflow to translate jailbreak papers into executable modules for immediate evaluation within a unified harness.
Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search

Xun Huang, Simeng Qin, Xiaoshuang Jia, Ranjie Duan, Huanqian Yan, Zhitao Zeng · Feb 26, 2026

Citations: 0
Red Team Automatic Metrics Multilingual
  • Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs.
  • To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module.
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Bang Zheng, Kyle Lam · Feb 25, 2026

Citations: 0
Expert Verification Automatic Metrics MedicineCoding
  • Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity.
  • We introduce MEDSYN, a multilingual, multimodal benchmark of highly complex clinical cases with up to 7 distinct visual clinical evidence (CE) types per case.
SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation

Jingzhuo Wu, Jiajun Zhang, Keyan Jin, Dehua Ma, Junbo Wang · Feb 23, 2026

Citations: 0
Automatic Metrics Multi Agent Multilingual
  • To address this, we introduce the Style-Adaptive Multi-Agent System (SAMAS), a novel framework that treats style preservation as a signal processing task.
  • Extensive experiments on translation benchmarks show that SAMAS achieves competitive semantic accuracy against strong baselines, primarily by leveraging its statistically significant advantage in style fidelity.
IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages

Priyaranjan Pattnayak, Sanchari Chowdhuri · Feb 18, 2026

Citations: 0
Red Team CodingMultilingual
  • Safety alignment of large language models (LLMs) is mostly evaluated in English and contract-bound, leaving multilingual vulnerabilities understudied.
  • We introduce Indic Jailbreak Robustness (IJR), a judge-free benchmark for adversarial safety across 12 Indic and South Asian languages (2.1 Billion speakers), covering 45216 prompts in JSON (contract-bound) and Free (naturalistic) tracks.
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…
Unlocking Reasoning Capability on Machine Translation in Large Language Models

Sara Rajaee, Sebastian Vincent, Alexandre Berard, Marzieh Fadaee, Kelly Marchisio, Tom Kocmi · Feb 16, 2026

Citations: 0
Critique Edit Long Horizon MathCoding
  • We systematically evaluate several open- and closed-weights RLMs on the WMT24++ benchmark and find that enabling explicit reasoning consistently degrades translation quality across languages and models.
Citations: 0
Pairwise Preference Automatic Metrics CodingMultilingual
  • Yet safety pipelines, benchmarks, and alignment still largely target English and a handful of high-resource languages, implicitly assuming safety and factuality ''transfer'' across languages.
  • We synthesize recent findings indicating that (i) safety guardrails weaken sharply on low-resource and code-mixed inputs, (ii) culturally harmful behavior can persist even when standard toxicity scores look acceptable, and (iii)…
Citations: 0
Pairwise Preference Long Horizon Multilingual
  • The methodological trajectory moves from classical supervised adaptation for task-specific demands to decoding-time alignment for safety, finally leveraging human feedback and preference modeling to achieve sociolinguistic acuity.
A Parallel Cross-Lingual Benchmark for Multimodal Idiomaticity Understanding

Dilara Torunoğlu-Selamet, Dogukan Arslan, Rodrigo Wilkens, Wei He, Doruk Eryiğit, Thomas Pickard · Jan 13, 2026

Citations: 0
Pairwise Preference Multilingual
  • The dataset, containing 34 languages and over ten thousand items, allows comparative analyses of idiomatic patterns among language-specific realisations and preferences in order to gather insights about shared cultural aspects.
  • The result is a high-quality benchmark for evaluating multilingual and multimodal idiomatic language understanding.
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Vaibhav Devraj, Dhruv Kumar, Jagat Sesh Challa, Parth Agarwal, Navya Kommuri, Trizal Garg · Dec 26, 2025

Citations: 0
Expert Verification Automatic Metrics CodingMultilingual
  • To investigate this potential capability gap, we present CricBench, a comprehensive benchmark suite for evaluating LLMs on specialized cricket data.
  • We evaluate six state-of-the-art models, including GPT-4o, Claude 3.7 Sonnet, and open-source models, using a strict evaluation protocol.
World Simulation with Video Foundation Models for Physical AI

NVIDIA, :, Arslan Ali, Junjie Bai, Maciej Bala, Yogesh Balaji · Oct 28, 2025

Citations: 0
Simulation Env Long Horizon CodingMultilingual
  • These capabilities enable more reliable synthetic data generation, policy evaluation, and closed-loop simulation for robotics and autonomous systems.
  • To accelerate research and deployment in Physical AI, we release source code, pretrained checkpoints, and curated benchmarks under the NVIDIA Open Model License at https://github.com/nvidia-cosmos/cosmos-predict2.5 and…
Estonian Native Large Language Model Benchmark

Helena Grete Lillepalu, Tanel Alumäe · Oct 24, 2025

Citations: 0
Human EvalLlm As Judge Multilingual
  • The availability of LLM benchmarks for the Estonian language is limited, and a comprehensive evaluation comparing the performance of different LLMs on Estonian tasks has yet to be conducted.
  • We introduce a new benchmark for evaluating LLMs in Estonian, based on seven diverse datasets.
MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

Chenxi Whitehouse, Sebastian Ruder, Tony Lin, Oksana Kurylo, Haruka Takagi, Janice Lam · Sep 30, 2025

Citations: 0
Pairwise PreferenceRubric Rating Automatic Metrics Multilingual
  • To address this, we introduce MENLO, a framework that operationalizes the evaluation of native-like response quality based on audience design-inspired mechanisms.
  • Additionally, we show that RL-trained judges can serve as generative reward models to enhance LLMs' multilingual proficiency, though discrepancies with human judgment remain.

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