Summary of Ask-eda: a Design Assistant Empowered by Llm, Hybrid Rag and Abbreviation De-hallucination, By Luyao Shi et al.
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucinationby Luyao Shi, Michael…
Summary of Should We Fine-tune or Rag? Evaluating Different Techniques to Adapt Llms For Dialogue, by Simone Alghisi et al.
Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogueby Simone Alghisi,…
Summary of Re-rag: Improving Open-domain Qa Performance and Interpretability with Relevance Estimator in Retrieval-augmented Generation, by Kiseung Kim et al.
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generationby Kiseung Kim,…
Summary of Multi-head Rag: Solving Multi-aspect Problems with Llms, by Maciej Besta et al.
Multi-Head RAG: Solving Multi-Aspect Problems with LLMsby Maciej Besta, Ales Kubicek, Roman Niggli, Robert Gerstenberger,…
Summary of Unioqa: a Unified Framework For Knowledge Graph Question Answering with Large Language Models, by Zhuoyang Li et al.
UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Modelsby Zhuoyang Li,…
Summary of Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost, by Masha Belyi et al.
Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low…
Summary of A Theory For Token-level Harmonization in Retrieval-augmented Generation, by Shicheng Xu et al.
A Theory for Token-Level Harmonization in Retrieval-Augmented Generationby Shicheng Xu, Liang Pang, Huawei Shen, Xueqi…
Summary of Clustered Retrieved Augmented Generation (crag), by Simon Akesson and Frances A. Santos
Clustered Retrieved Augmented Generation (CRAG)by Simon Akesson, Frances A. SantosFirst submitted to arxiv on: 24…
Summary of Enhancing Noise Robustness Of Retrieval-augmented Language Models with Adaptive Adversarial Training, by Feiteng Fang et al.
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Trainingby Feiteng Fang, Yuelin Bai,…
Summary of Two-layer Retrieval-augmented Generation Framework For Low-resource Medical Question Answering Using Reddit Data: Proof-of-concept Study, by Sudeshna Das et al.
Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Studyby Sudeshna…