Summary of Learning to Verify Summary Facts with Fine-grained Llm Feedback, by Jihwan Oh et al.
Learning to Verify Summary Facts with Fine-Grained LLM Feedbackby Jihwan Oh, Jeonghwan Choi, Nicole Hee-Yeon…
Learning to Verify Summary Facts with Fine-Grained LLM Feedbackby Jihwan Oh, Jeonghwan Choi, Nicole Hee-Yeon…
Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Featuresby Muhammad…
AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Frameworkby Meihao Fan, Ju Fan, Nan…
The Parameters of Educabilityby Leslie G. ValiantFirst submitted to arxiv on: 12 Dec 2024CategoriesMain: Artificial…
Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Clinical Pathology Analysisby Shengxuming Zhang,…
PolyIPA – Multilingual Phoneme-to-Grapheme Conversion Modelby Davor LaucFirst submitted to arxiv on: 12 Dec 2024CategoriesMain:…
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLIby Sourav Banerjee, Anush…
Inference-Time Diffusion Model Distillationby Geon Yeong Park, Sang Wan Lee, Jong Chul YeFirst submitted to…
Superficial Consciousness Hypothesis for Autoregressive Transformersby Yosuke Miyanishi, Keita MitaniFirst submitted to arxiv on: 10…
I See, Therefore I Do: Estimating Causal Effects for Image Treatmentsby Abhinav Thorat, Ravi Kolla,…