Summary of Trutheval: a Dataset to Evaluate Llm Truthfulness and Reliability, by Aisha Khatun and Daniel G. Brown
TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliabilityby Aisha Khatun, Daniel G. BrownFirst submitted…
TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliabilityby Aisha Khatun, Daniel G. BrownFirst submitted…
Fruit Classification System with Deep Learning and Neural Architecture Searchby Christine Dewi, Dhananjay Thiruvady, Nayyar…
HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Taskby Yu Tian, Tianqi Shao,…
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CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Modelsby Junho Kim, Hyunjun Kim,…
Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMsby Nik Bear BrownFirst submitted to…
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Zyda: A 1.3T Dataset for Open Language Modelingby Yury Tokpanov, Beren Millidge, Paolo Glorioso, Jonathan…
Personalized Topic Selection Model for Topic-Grounded Dialogueby Shixuan Fan, Wei Wei, Xiaofei Wen, Xianling Mao,…
Position Debiasing Fine-Tuning for Causal Perception in Long-Term Dialogueby Shixuan Fan, Wei Wei, Wendi Li,…