Human Feedback and Eval Paper Explorer

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

• A board-certified radiologist and a radiology resident independently performed blinded pairwise evaluations across 4 criteria: terminology accuracy, readability, overall quality, and radiologist-style authenticity.
• Radiologist 2 rated readability as equivalent in 75% of cases and favored the human-edited translation for overall quality (40% vs 21%).

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

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

• Translating these systems into clinical practice requires assessment in real-world workflows with rigorous safety oversight.
• We sought to assess the conversational safety and quality, patient and clinician experience, and clinical reasoning capabilities compared to primary care providers (PCPs).

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

• To bridge the gap between existing evaluations and practical use, we introduce LIT-RAGBench (the Logic, Integration, Table, Reasoning, and Abstention RAG Generator Benchmark), which defines five categories: Integration, Reasoning, Logic,…
• We use LLM-as-a-Judge for scoring and report category-wise and overall accuracy.

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

• 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.

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

• 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.

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

• 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.

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

• In this paper, we introduce LaTeXTrans, a collaborative multi-agent system designed to address this challenge.
• LaTeXTrans ensures format preservation, structural fidelity, and terminology consistency through six specialized agents: 1) a Parser that decomposes LaTeX into translation-friendly units via placeholder substitution and syntax filtering; 2)…

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

• We introduce MuRating, a scalable framework that transfers high-quality English data-quality signals into a single rater for 17 target languages.
• Compared to strong baselines, including QuRater, AskLLM, DCLM and so on, our approach boosts average accuracy on both English benchmarks and multilingual evaluations, with especially large gains on knowledge-intensive tasks.

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

• Computer-using agents (CUAs) are becoming increasingly capable; however, it remains difficult to scale evaluation of whether a trajectory truly fulfills a user instruction.
• In this work, we study reward modeling from execution video: a sequence of keyframes from an agent trajectory that is independent of the agent's internal reasoning or actions.

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

• 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.

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

• Building on the information bottleneck principle, we conceptualize explanations as compressed representations that retain only the information essential for producing correct answers.To operationalize this view, we introduce an evaluation…

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

• On benchmarks spanning city names, person names, organizations, multilingual political parties, and bibliographic records, EnsembleLink matches or exceeds methods requiring extensive labeling.

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

• Our benchmark is built on a user manual for an agricultural machine, available in English, French, and German.
• The evaluation focuses on realistic "needle-in-a-haystack" challenges and includes unanswerable questions to test for hallucinations.

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

• Experiments on causality, physics, and chemistry benchmarks demonstrate that RefTool outperforms existing tool-creation and domain-specific reasoning methods by 12.3% on average accuracy, while being cost-efficient and broadly generalizable…

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

• We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base.
• Our results reveal a substantial performance gap: Gemini-3-Pro achieves 58.1% execution accuracy, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%.

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

• Large language models increasingly support multiple languages, yet most benchmarks for gender bias remain English-centric.
• Human evaluations confirm that our data generation method results in high accuracy of both translations and gender labels across languages.

Match reason: Matched by broad semantic/index fallback.

• To systematically investigate the reliability of MT metrics, we first curate a meta-evaluation dataset focused on non-literal translations, namely MENT.
• To mitigate these limitations, we propose RATE, a novel agentic translation evaluation framework, centered by a reflective Core Agent that dynamically invokes specialized sub-agents.