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Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models (LLMs) perform Question Answering (QA) tasks in various domains.
Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Banerjee, S. and Lavie, A · 2005
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A tale of four metrics
Connor, R · 2016
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Ethayarajh, K · 2019
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Bertscore: Evaluating text generation with bert
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., and Artzi, Y · 2019
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al · 2020
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On the sentence embeddings from pre-trained language models
Li, B., Zhou, H., He, J., Wang, M., Yang, Y., and Li, L · 2020
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On the interplay between fine-tuning and sentence-level probing for linguistic knowledge in pre-trained transformers
Mosbach, M., Khokhlova, A., Hedderich, M. A., and Klakow, D · 2020
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Bidirectional encoder representations from transformers (bert) for question answering in the telecom domain.: Adapting a bert-like language model to the telecom domain using the electra pre-training approach, 2021
Holm, H · 2021
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Timkey, W. and Van Schijndel, M · 2021
Cited alongside, same era.
Understanding retrieval augmentation for long-form question answering
Chen, H.-T., Xu, F., Arora, S. A., and Choi, E · 2023
Cited alongside, same era.
Menli: Robust evaluation metrics from natural language inference
Chen, Y. and Eger, S · 2023
Cited alongside, same era.
Ragas: Automated evaluation of retrieval augmented generation
Es, S., James, J., Espinosa-Anke, L., and Schockaert, S · 2023
Cited alongside, same era.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
3GPP release 15
3GPP · 2024
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Benchmarking large language models in retrieval-augmented generation
Chen, J., Lin, H., Han, X., and Sun, L · 2024
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Automated evaluation of retrieval-augmented language models with task-specific exam generation
Guinet, G., Omidvar-Tehrani, B., Deoras, A., and Callot, L · 2024
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Using large language models to understand telecom standards
Karapantelakis, A., Shakur, M., Nikou, A., Moradi, F., Orlog, C., Gaim, F., Holm, H., Nimara, D. D., and Huang, V · 2024
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Building and evaluating advanced rag applications, 2024
Liu, J. and Datta, A · 2024
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Mistral-finetune
MistralAI · 2024
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Cited alongside, same era.
Ares: An automated evaluation framework for retrieval-augmented generation systems
Saad-Falcon, J., Khattab, O., Potts, C., and Zaharia, M · 2023
Cited alongside, same era.
Observations on llms for telecom domain: capabilities and limitations
Soman, S. and Ranjani, H. G · 2023
Cited alongside, same era.
C-pack: Packaged resources to advance general chinese embedding, 2023
Xiao, S., Liu, Z., Zhang, P., and Muennighoff, N · 2023
Cited alongside, same era.
Retrieve anything to augment large language models, 2023
Zhang, P., Xiao, S., Liu, Z., Dou, Z., and Nie, J.-Y · 2023
Cited alongside, same era.
Top-k cosine similarity interesting pairs search
Zhu, S., Wu, J., and Xia, G · 2023
Cited alongside, same era.
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Observations on building rag systems for technical documents
Soman, S. and Roychowdhury, S · 2024
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Is cosine-similarity of embeddings really about similarity?
Steck, H., Ekanadham, C., and Kallus, N · 2024
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Crag–comprehensive rag benchmark
Yang, X., Sun, K., Xin, H., Sun, Y., Bhalla, N., Chen, X., Choudhary, S., Gui, R. D., Jiang, Z. W., Jiang, Z., et al · 2024
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Zhou, H., Hu, C., Yuan, Y., Cui, Y., Jin, Y., Chen, C., Wu, H., Yuan, D., Jiang, L., Wu, D., et al · 2024
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