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Chunking information is a key step in Retrieval Augmented Generation (RAG).
1908
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Juge, R., Bentabet, I., Ferradans, S.: The fintoc-2019 shared task: Financial document structure extraction. In: Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019). pp. 51–57 (2019)
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Bentabet, N.I., Juge, R., El Maarouf, I., Mouilleron, V., Valsamou-Stanislawski, D., El-Haj, M.: The financial document structure extraction shared task (fintoc 2020). In: Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation. pp. 13–22 (2020)
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El Maarouf, I., Kang, J., Azzi, A.A., Bellato, S., Gan, M., El-Haj, M.: The financial document structure extraction shared task (FinTOC2021). In: Proceedings of the 3rd Financial Narrative Processing Workshop. pp. 111–119 (2021)
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Liu, Z., Huang, D., Huang, K., Li, Z., Zhao, J.: Finbert: A pre-trained financial language representation model for financial text mining. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence. pp. 4513–4519 (2021)
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Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 697–706 (2021)
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Chen, Z., Li, S., Smiley, C., Ma, Z., Shah, S., Wang, W.Y.: ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering (2022)
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Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.: Doclaynet: A large human-annotated dataset for document-layout segmentation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 3743–3751 (2022)
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Shah, R.S., Chawla, K., Eidnani, D., Shah, A., Du, W., Chava, S., Raman, N., Smiley, C., Chen, J., Yang, D.: WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain (2022)
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Anantha, R., Bethi, T., Vodianik, D., Chappidi, S.: Context Tuning for Retrieval Augmented Generation (2023)
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Moore, S., Nguyen, H.A., Chen, T., Stamper, J.: Assessing the quality of multiple-choice questions using gpt-4 and rule-based methods. In: European Conference on Technology Enhanced Learning. pp. 229–245. Springer (2023)
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Naismith, B., Mulcaire, P., Burstein, J.: Automated evaluation of written discourse coherence using gpt-4. In: Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023). pp. 394–403 (2023)
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Singh Phogat, K., Harsha, C., Dasaratha, S., Ramakrishna, S., Akhil Puranam, S.: Zero-Shot Question Answering over Financial Documents using Large Language Models. arXiv e-prints pp. arXiv–2311 (2023)
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Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y.J., Madotto, A., Fung, P.: Survey of Hallucination in Natural Language Generation. ACM Computing Surveys 55
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2023
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Kaur, S., Smiley, C., Gupta, A., Sain, J., Wang, D., Siddagangappa, S., Aguda, T., Shah, S.: REFinD: Relation Extraction Financial Dataset. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’23, ACM (Jul 2023). https://doi.org/10.1145/3539618.3591911, http://dx.doi.org/10.1145/3539618.3591911
2023
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Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: A Large Language Model for Finance (2023)
2023
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Xu, P., Ping, W., Wu, X., McAfee, L., Zhu, C., Liu, Z., Subramanian, S., Bakhturina, E., Shoeybi, M., Catanzaro, B.: Retrieval meets Long Context Large Language Models (2023)
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Yang, H., Liu, X.Y., Wang, C.D.: FinGPT: Open-Source Financial Large Language Models (2023)
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Ye, H., Liu, T., Zhang, A., Hua, W., Jia, W.: Cognitive Mirage: A Review of Hallucinations in Large Language Models (2023)
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Zhang, B., Yang, H., Liu, X.Y.: Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models (2023)
2023
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Balaguer, A., Benara, V., de Freitas Cunha, R.L., de M. Estevão Filho, R., Hendry, T., Holstein, D., Marsman, J., Mecklenburg, N., Malvar, S., Nunes, L.O., Padilha, R., Sharp, M., Silva, B., Sharma, S., Aski, V., Chandra, R.: Rag vs fine-tuning: Pipelines, tradeoffs, and a case study on agriculture (2024)
2024
Closest in time.
Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., Abdelrazek, M.: Seven Failure Points When Engineering a Retrieval Augmented Generation System (2024)
2024
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Jiang, A.Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D.S., de las Casas, D., Hanna, E.B., Bressand, F., Lengyel, G., Bour, G., Lample, G., Lavaud, L.R., Saulnier, L., Lachaux, M.A., Stock, P., Subramanian, S., Yang, S., Antoniak, S., Scao, T.L., Gervet, T., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mixtral of Experts (2024)
2024
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llmware: Rag Instruct Benchmark Tester. https://huggingface.co/datasets/llmware/rag_instruct_benchmark_tester , Accessed: January 15, 2024
2024
Closest in time.
Retteter, J.: Mastering Table Extraction: Revolutionize Your Earnings Reports Analysis with AI. https://medium.com/unstructured-io/mastering-table-extraction-revolutionize-your-earnings-reports-analysis-with-ai-1bc32c22720e , Accessed: January 15, 2024
2024
Closest in time.
Rizinski, M., Peshov, H., Mishev, K., Jovanovik, M., Trajanov, D.: Sentiment analysis in finance: From transformers back to explainable lexicons (xlex). IEEE Access 12
2024
Closest in time.