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@@ -19,3 +19,11 @@ Precise Zero-Shot Dense Retrieval without Relevance Labels,RAG,"While dense retr
 Corrective Retrieval Augmented Generation,RAG,"Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a practicable complement to LLMs, it relies heavily on the relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. To this end, we propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. Specifically, a lightweight retrieval evaluator is designed to assess the overall quality of retrieved documents for a query, returning a confidence degree based on which different knowledge retrieval actions can be triggered. Since retrieval from static and limited corpora can only return sub-optimal documents, large-scale web searches are utilized as an extension for augmenting the retrieval results. Besides, a decompose-then-recompose algorithm is designed for retrieved documents to selectively focus on key information and filter out irrelevant information in them. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches. Experiments on four datasets covering short- and long-form generation tasks show that CRAG can significantly improve the performance of RAG-based approaches.","The paper introduces Corrective Retrieval Augmented Generation (CRAG), a novel approach designed to enhance the robustness of large language models (LLMs) by addressing the issue of hallucinations and inaccuracies that arise from reliance on retrieved documents. CRAG incorporates a lightweight retrieval evaluator that assesses the quality of retrieved documents and triggers corrective actions based on their relevance, categorized as Correct, Incorrect, or Ambiguous. When the retrieved documents are deemed correct, they undergo a knowledge refinement process to extract essential information. Conversely, if they are incorrect, CRAG resorts to large-scale web searches for supplementary knowledge. The method is designed to be plug-and-play, allowing it to be integrated seamlessly with existing retrieval-augmented generation frameworks.  Experimental results across four diverse datasets demonstrate that CRAG significantly improves the performance of standard retrieval-augmented generation (RAG) and state-of-the-art approaches like Self-RAG. The findings highlight CRAG's adaptability and generalizability in both short- and long-form generation tasks, showcasing its effectiveness in mitigating the challenges posed by inaccurate retrievals. The paper concludes by emphasizing the importance of self-correction mechanisms in enhancing the reliability of generative models while acknowledging the need for further advancements in retrieval evaluation capabilities.",8.5,https://arxiv.org/abs/2401.15884,https://doi.org/10.48550/arXiv.2401.15884,"@misc{yan2024correctiveretrievalaugmentedgeneration,       title={Corrective Retrieval Augmented Generation},        author={Shi-Qi Yan and Jia-Chen Gu and Yun Zhu and Zhen-Hua Ling},       year={2024},       eprint={2401.15884},       archivePrefix={arXiv},       primaryClass={cs.CL},       url={https://arxiv.org/abs/2401.15884},  }",,Yes
 "Re2G: Retrieve, Rerank, Generate",RAG,"As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation. Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval. To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker, and generation using only ground truth on the target sequence output. We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact-checking, and dialog, with relative gains of 9% to 34% over the previous state-of-the-art on the KILT leaderboard. We make our code available as open source at  https://github.com/IBM/kgi-slot-filling/tree/re2g.","The paper presents a novel approach called Re2G (Retrieve, Rerank, Generate), which enhances the performance of generative language models by integrating retrieval and reranking mechanisms into a BART-based sequence-to-sequence generation framework. The authors argue that while large transformer models like GPT-3 and T5 have shown impressive capabilities, they can be further improved by leveraging non-parametric memory through retrieval from a corpus of passages. Re2G combines neural initial retrieval with a reranking process that allows for the merging of results from different retrieval methods, such as BM25 and neural approaches, thereby improving the quality of the generated outputs. The system is trained end-to-end using a novel variation of knowledge distillation, which utilizes only the ground truth of the target sequence output.  The experimental results demonstrate significant improvements across four diverse tasks—zero-shot slot filling, question answering, fact checking, and dialog—achieving relative gains of 9% to 34% over previous state-of-the-art models on the KILT leaderboard. The paper highlights the effectiveness of the reranking mechanism and the benefits of ensembling retrieval methods, ultimately establishing Re2G as a leading approach in knowledge-intensive natural language processing tasks. The authors have made their code available as open source to facilitate further research and development in this area.",7,https://aclanthology.org/2022.naacl-main.194/,https://doi.org/10.18653/v1/2022.naacl-main.194,"@inproceedings{glass-etal-2022-re2g,     title = ""{R}e2{G}: Retrieve, Rerank, Generate"",     author = ""Glass, Michael  and       Rossiello, Gaetano  and       Chowdhury, Md Faisal Mahbub  and       Naik, Ankita  and       Cai, Pengshan  and       Gliozzo, Alfio"",     editor = ""Carpuat, Marine  and       de Marneffe, Marie-Catherine  and       Meza Ruiz, Ivan Vladimir"",     booktitle = ""Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"",     month = jul,     year = ""2022"",     address = ""Seattle, United States"",     publisher = ""Association for Computational Linguistics"",     url = ""https://aclanthology.org/2022.naacl-main.194/"",     doi = ""10.18653/v1/2022.naacl-main.194"",     pages = ""2701--2715"",     abstract = ""As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation. Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval. To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker and generation using only ground truth on the target sequence output. We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact checking and dialog, with relative gains of 9{\%} to 34{\%} over the previous state-of-the-art on the KILT leaderboard. We make our code available as open source."" }",,Yes
 Active Retrieval Augmented Generation,RAG,"Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augmenting LMs by retrieving information from external knowledge resources is one promising solution. Most existing retrieval augmented LMs employ a retrieve-and-generate setup that only retrieves information once based on the input. This is limiting, however, in more general scenarios involving generation of long texts, where continually gathering information throughout generation is essential. In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation. We propose Forward-Looking Active REtrieval augmented generation (FLARE), a generic method which iteratively uses a prediction of the upcoming sentence to anticipate future content, which is then utilized as a query to retrieve relevant documents to regenerate the sentence if it contains low-confidence tokens. We test FLARE along with baselines comprehensively over 4 long-form knowledge-intensive generation tasks/datasets. FLARE achieves superior or competitive performance on all tasks, demonstrating the effectiveness of our method. Code and datasets are available at https://github.com/jzbjyb/FLARE.","The paper presents a novel approach called Forward-Looking Active Retrieval Augmented Generation (FLARE), which enhances the capabilities of large language models (LMs) by integrating an active retrieval mechanism during the text generation process. Traditional retrieval-augmented LMs typically retrieve information only once based on the initial input, which can be limiting for long-form generation tasks that require ongoing access to relevant information. FLARE addresses this limitation by allowing the model to actively decide when and what to retrieve based on the confidence of the generated content. By predicting the upcoming sentence and using it as a query for retrieval, FLARE can gather additional information dynamically, thereby improving the accuracy and relevance of the generated text.  The authors conducted comprehensive experiments across four knowledge-intensive long-form generation tasks, demonstrating that FLARE outperforms existing retrieval methods, including both single-time and multi-time retrieval baselines. The results indicate that FLARE's active retrieval strategy significantly enhances the model's performance, particularly in tasks requiring complex reasoning and information synthesis. The paper concludes by highlighting the effectiveness and generalizability of FLARE, suggesting future directions for improving active retrieval strategies and developing efficient architectures for integrating information retrieval with language generation.",9,https://aclanthology.org/2023.emnlp-main.495.pdf,https://doi.org/10.18653/v1/2023.emnlp-main.495,"@inproceedings{jiang-etal-2023-active,     title = ""Active Retrieval Augmented Generation"",     author = ""Jiang, Zhengbao  and       Xu, Frank  and       Gao, Luyu  and       Sun, Zhiqing  and       Liu, Qian  and       Dwivedi-Yu, Jane  and       Yang, Yiming  and       Callan, Jamie  and       Neubig, Graham"",     editor = ""Bouamor, Houda  and       Pino, Juan  and       Bali, Kalika"",     booktitle = ""Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing"",     month = dec,     year = ""2023"",     address = ""Singapore"",     publisher = ""Association for Computational Linguistics"",     url = ""https://aclanthology.org/2023.emnlp-main.495/"",     doi = ""10.18653/v1/2023.emnlp-main.495"",     pages = ""7969--7992"",     abstract = ""Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augmenting LMs by retrieving information from external knowledge resources is one promising solution. Most existing retrieval augmented LMs employ a retrieve-and-generate setup that only retrieves information once based on the input. This is limiting, however, in more general scenarios involving generation of long texts, where continually gathering information throughout generation is essential. In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation. We propose Forward-Looking Active REtrieval augmented generation (FLARE), a generic method which iteratively uses a prediction of the upcoming sentence to anticipate future content, which is then utilized as a query to retrieve relevant documents to regenerate the sentence if it contains low-confidence tokens. We test FLARE along with baselines comprehensively over 4 long-form knowledge-intensive generation tasks/datasets. FLARE achieves superior or competitive performance on all tasks, demonstrating the effectiveness of our method."" } ",,Yes
+,,,,,https://dl.acm.org/doi/pdf/10.1145/3673791.3698415,,,,
+,,,,,https://dl.acm.org/doi/pdf/10.1145/3626772.3657957,,,,
+Benchmarking Large Language Models in Retrieval-Augmented Generation,,,,,file:///D:/Downloads/29728-Article%20Text-33782-1-2-20240324%20(1).pdf,,,,
+,,,,,https://aclanthology.org/2020.emnlp-main.437.pdf,,,,
+,,,,,https://arxiv.org/pdf/2205.01230,,,,
+,,,,,https://arxiv.org/pdf/2311.07914,,,,
+,,,,,https://arxiv.org/pdf/2104.07567,,,,
+,,,,,https://proceedings.neurips.cc/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf,,,,