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Found 4 papers, 1 papers with code
ERATTA: Extreme RAG for Table To Answers with Large Language Models
Large language models (LLMs) with residual augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions in the recent past.
Automated Heterogeneous Low-Bit Quantization of Multi-Model Deep Learning Inference Pipeline
Multiple Deep Neural Networks (DNNs) integrated into single Deep Learning (DL) inference pipelines e. g. Multi-Task Learning (MTL) or Ensemble Learning (EL), etc., albeit very accurate, pose challenges for edge deployment.
Hallucination-minimized Data-to-answer Framework for Financial Decision-makers
Large Language Models (LLMs) have been applied to build several automation and personalized question-answering prototypes so far.
Cross-Lingual Training for Automatic Question Generation
For a new language, such training instances are hard to obtain making the QG problem even more challenging.
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