Search: “evaluation”

arXiv:2508.07849v2 Announce Type: replace Abstract: Despite advances in legal NLP, no comprehensive evaluation of Transformer-based models customized for legal tasks (referred to as `legal-specific' m

arXiv:2602.08023v3 Announce Type: replace-cross Abstract: Existing benchmarks for LLM-based offensive security agents use isolated, single-target setups with a known vulnerable service and fixed objec

arXiv:2605.21063v1 Announce Type: new Abstract: Typical LLM responses tend to follow a default style, even though users often have distinct preferences regarding tone, verbosity, and formality that th

arXiv:2605.21443v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly being explored for video game quality assurance, especially gameplay glitch detection. Most existing eval

arXiv:2603.28675v2 Announce Type: replace Abstract: Facial recognition systems are increasingly deployed in law enforcement and security contexts, where algorithmic decisions can carry significant soc

arXiv:2605.19855v1 Announce Type: cross Abstract: Concept-based Explainable Artificial Intelligence (XAI) interprets deep learning models using human-understandable visual features (e.g., textures or

arXiv:2605.18827v1 Announce Type: cross Abstract: Multiple-choice QA benchmarks usually evaluate small language models (SLMs) as direct answerers, but deployed language-model systems increasingly rely

arXiv:2511.23253v3 Announce Type: replace Abstract: Recent advancements in Vision-Language Models (VLMs) have significantly impacted various industries. In agriculture, these multimodal capabilities h

arXiv:2605.18667v1 Announce Type: new Abstract: Earth embedding models transform Earth observation data into embeddings uniquely tied to locations on the Earth's surface. These models are typically ev

arXiv:2605.17370v1 Announce Type: new Abstract: Cognitive behavioural therapy is widely used to help patients understand and manage psychological distress. It is often delivered through spoken convers

arXiv:2605.18498v1 Announce Type: cross Abstract: Expert specialization in Mixture-of-Experts (MoE) models remains poorly understood, with traditional evaluations conflating architectural load-balanci

arXiv:2502.04055v2 Announce Type: replace Abstract: Current evaluations of synthetic tabular data mainly focus on how well joint distributions are modeled, often overlooking the assessment of their ef

arXiv:2605.18663v1 Announce Type: new Abstract: As LLM benchmarks saturate, the evaluation community has pursued two strategies to increase difficulty: escalating knowledge demands (GPQA, HLE) or remo

arXiv:2605.17932v1 Announce Type: cross Abstract: Prompt compression reduces inference cost and context length in large language models, but prior evaluations focus primarily on autoregressive archite

arXiv:2605.14488v1 Announce Type: new Abstract: Large Language Models (LLMs) augmented with Retrieval-Augmented Generation (RAG) techniques are revolutionizing applications across multiple domains, su

arXiv:2605.10186v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into legal drafting and research workflows, where incorrect citations or fabricated precedent

arXiv:2605.10820v1 Announce Type: new Abstract: Scientific discovery is fundamentally a resource-constrained process that requires navigating complex trade-offs between the quality and quantity of mea

arXiv:2605.08437v1 Announce Type: cross Abstract: Existing benchmarks for legal AI focus primarily on tasks where LLMs must produce legal arguments or documents, yet the capacity to judge such argumen

arXiv:2605.07926v1 Announce Type: new Abstract: As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar wo

arXiv:2605.07046v1 Announce Type: cross Abstract: Evaluation of large language models (LLMs) is increasingly critical, yet standard benchmarking methods rely on average accuracy, overlooking both the

arXiv:2605.07251v1 Announce Type: new Abstract: Large Language Models (LLMs) have become increasingly capable as tool-using agents, with benchmarks spanning diverse general agentic tasks. Yet rigorous

arXiv:2512.15567v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and

arXiv:2605.07675v1 Announce Type: new Abstract: We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs

arXiv:2605.04018v1 Announce Type: new Abstract: Reasoning-intensive retrieval aims to surface evidence that supports downstream reasoning rather than merely matching topical similarity. This capabilit

arXiv:2409.14500v5 Announce Type: replace Abstract: Although data that can be naturally represented as graphs is widespread in real-world applications across diverse industries, popular graph ML bench

arXiv:2604.27470v1 Announce Type: new Abstract: Millions of clinicians use ChatGPT to support clinical care, but evaluations of the most common use cases in model-clinician conversations are limited.

arXiv:2604.25359v1 Announce Type: new Abstract: Large Language Models are increasingly being deployed to extract structured data from unstructured and semi-structured sources: parsing invoices, medica

arXiv:2509.20374v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated strong performance across general NLP tasks, but their utility in automating numerical experime

arXiv:2406.10185v2 Announce Type: replace Abstract: Large Vision Language Models (LVLMs) are increasingly integral to healthcare applications, including medical visual question answering and imaging r

arXiv:2604.23347v1 Announce Type: new Abstract: We present a comprehensive evaluation of Large Language Models (LLMs) on Computer Science (CS) Data Structure examination questions. Our work introduces

arXiv:2604.23813v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance in Visually Rich Document Understanding (VRDU) tasks, but their capabili

arXiv:2604.21006v1 Announce Type: new Abstract: We introduce Deep FinResearch Bench, a practical and comprehensive evaluation framework for deep research (DR) agents in financial investment research.

arXiv:2604.21916v1 Announce Type: new Abstract: As frontier language models attain near-ceiling performance on static mathematical benchmarks, existing evaluations are increasingly unable to different

arXiv:2508.17761v3 Announce Type: replace Abstract: In safety-critical applications data-driven models must not only be accurate but also provide reliable uncertainty estimates. This property, commonl

arXiv:2604.18660v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior work

arXiv:2604.19510v1 Announce Type: new Abstract: Variability in illumination is a primary factor limiting deep learning robustness for field-based plant disease detection. This study evaluates Histogra

arXiv:2604.19526v1 Announce Type: cross Abstract: Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious

arXiv:2604.19092v1 Announce Type: cross Abstract: Recent advances in large-scale video world models have enabled increasingly realistic future prediction, raising the prospect of leveraging imagined v

arXiv:2510.19028v3 Announce Type: replace Abstract: As LLMs are increasingly deployed in real-world interactions, their social reasoning in interpersonal communication becomes critical. To explore the

arXiv:2508.17458v2 Announce Type: replace Abstract: Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component

arXiv:2602.06663v2 Announce Type: replace Abstract: Unified multimodal models (UMMs) have shown impressive capabilities in generating natural images and supporting multimodal reasoning. However, their

arXiv:2510.09351v2 Announce Type: replace Abstract: While Small Language Models (SLMs) have demonstrated promising performance on an increasingly wide array of commonsense reasoning benchmarks, curren

arXiv:2604.16279v1 Announce Type: new Abstract: Large Language Models (LLMs) have the potential to accelerate small molecule drug design due to their ability to reason about information from diverse s

arXiv:2604.06484v2 Announce Type: replace Abstract: Cultural values are expressed not only through language but also through visual scenes and everyday social practices. Yet existing evaluations of cu

arXiv:2510.09536v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in multilingual, real-world applications with user inputs -- naturally introducing typographi

arXiv:2604.12447v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models inherit rich world knowledge from vision-language backbones and acquire executable skills via action demonstrations.