AI 13
- Paper Review - “Dataset Condenstaion via Efficient Synthetic-Data Parameterization” by Jang-Hyun Kim et al., 2022
- Paper Review - [ICLR 2024] “SuRe ; Summarizing Retrievals using Answer Candidates for Open-Domain QA of LLMs”, Jaehyung Kim et al., 16 Jan 2024
- Paper Review - [ICLR 2024] "Self-RAG ; Learning To Retrieve, Generate, and Critique Through Self-Reflection", Akari Asai et al., Oct 2023
- Paper Review - [NAACL 2024] "Adaptive-RAG ; Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity", Soyeong Jeong et al., Mar 2024
- Paper Review - "CHAIN-OF-NOTE ; ENHANCING ROBUSTNESS IN RETRIEVAL-AUGMENTED LANGUAGE MODELS", Wenhao Yu et al., Nov 2023
- Paper Review - [ICLR 2021] "Distilling Knowledge From Reader To Retriever For Question Answering", Gautier Izacard et al., 4 Aug 2022
- Paper Review - [NIPS 2017] "Attention Is All You Need" by Ashish Vaswani et al., 2017
- Paper Review - "HiQA ; A Hierarchical Contextual Augmentation RAG for Massive Documents QA", Xinyue Chen et al., Feb 2024
- Paper Review - [EMNLP-Findings 2023] "Enhancing Abstractiveness of Summarization Models through Calibrated Distillation" by Hwanjun Song et al., Dec 2023
- Paper Review - "The Power of Noise - Redefining Retrieval for RAG Systems" by Cuconasu et al., 2024
- Paper Review - [NIPS 2014] "Sequence to Sequence Learning with Neural Networks" by Sutskever et al., 2014
- Paper Review - [EMNLP 2014]"GloVe, Global Vectors for Word Representation" by Pennington et al., 2014
- Paper Review - [NIPS 2013] "Distributed Representations of Words and Phrases and their Compositionality" by Mikolov et al., 2013