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Showing 201–225 of 1107 papers

TitleStatusHype
On the Reasoning Capacity of AI Models and How to Quantify It—0
The AI Penalization Effect: People Reduce Compensation for Workers Who Use AI—0
Patent Figure Classification using Large Vision-language ModelsCode0
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction—0
MedS^3: Towards Medical Small Language Models with Self-Evolved Slow ThinkingCode2
Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No!—0
FaceXBench: Evaluating Multimodal LLMs on Face UnderstandingCode1
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong—0
Vision-Language Models Do Not Understand Negation—0
Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework—0
Towards Multilingual LLM Evaluation for Baltic and Nordic languages: A study on Lithuanian History—0
ToMATO: Verbalizing the Mental States of Role-Playing LLMs for Benchmarking Theory of MindCode1
Rethinking AI Cultural Alignment—0
Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation—0
ZNO-Eval: Benchmarking reasoning capabilities of large language models in UkrainianCode1
First Token Probability Guided RAG for Telecom Question Answering—0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQsCode0
DRIVINGVQA: Analyzing Visual Chain-of-Thought Reasoning of Vision Language Models in Real-World Scenarios with Driving Theory Tests—0
Knowledge Retrieval Based on Generative AI—0
Localizing AI: Evaluating Open-Weight Language Models for Languages of Baltic States—0
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model EvaluationCode1
(WhyPHI) Fine-Tuning PHI-3 for Multiple-Choice Question Answering: Methodology, Results, and ChallengesCode0
CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering—0
Unifying Specialized Visual Encoders for Video Language ModelsCode1
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