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In this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-augmented generation (RAG) and conversational question answering (QA).
Compositional semantic parsing on semi-structured tables
Pasupat, P. and Liang, P · 2015
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Ms marco: A human generated machine reading comprehension dataset
Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., and Deng, L · 2016
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Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L · 2017
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Newsqa: A machine comprehension dataset
Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., and Suleman, K · 2017
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Quac: Question answering in context
Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-t., Choi, Y., Liang, P., and Zettlemoyer, L · 2018
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The narrativeqa reading comprehension challenge
Kočiskỳ, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K. M., Melis, G., and Grefenstette, E · 2018
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Know what you don’t know: Unanswerable questions for squad
Rajpurkar, P., Jia, R., and Liang, P · 2018
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Interpretation of natural language rules in conversational machine reading
Saeidi, M., Bartolo, M., Lewis, P., Singh, S., Rocktäschel, T., Sheldon, M., Bouchard, G., and Riedel, S · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., and Manning, C. D · 2018
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Quoref: A reading comprehension dataset with questions requiring coreferential reasoning
Dasigi, P., Liu, N. F., Marasović, A., Smith, N. A., and Gardner, M · 2019
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., and Gardner, M · 2019
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Can you unpack that? learning to rewrite questions-in-context
Elgohary, A., Peskov, D., and Boyd-Graber, J · 2019
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Eli5: Long form question answering
Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., and Auli, M · 2019
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Natural questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al · 2019
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Reasoning over paragraph effects in situations
Lin, K., Tafjord, O., Clark, P., and Gardner, M · 2019
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Coqa: A conversational question answering challenge
Reddy, S., Chen, D., and Manning, C. D · 2019
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Doqa-accessing domain-specific faqs via conversational qa
Campos, J. A., Otegi, A., Soroa, A., Deriu, J. M., Cieliebak, M., and Agirre, E · 2020
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How to ask better questions? a large-scale multi-domain dataset for rewriting ill-formed questions
Chu, Z., Chen, M., Chen, J., Wang, M., Gimpel, K., Faruqui, M., and Si, X · 2020
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doc2dial: A goal-oriented document-grounded dialogue dataset
Feng, S., Wan, H., Gunasekara, C., Patel, S., Joshi, S., and Lastras, L · 2020
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Open-retrieval conversational question answering
Qu, C., Yang, L., Chen, C., Qiu, M., Croft, W. B., and Iyyer, M · 2020
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Few-shot generative conversational query rewriting
Yu, S., Liu, J., Yang, J., Xiong, C., Bennett, P., Gao, J., and Liu, Z · 2020
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Building and evaluating open-domain dialogue corpora with clarifying questions
Aliannejadi, M., Kiseleva, J., Chuklin, A., Dalton, J., and Burtsev, M · 2021
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Open-domain question answering goes conversational via question rewriting
Anantha, R., Vakulenko, S., Tu, Z., Longpre, S., Pulman, S., and Chappidi, S · 2021
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Question rewriting for open-domain conversational qa: Best practices and limitations
Del Tredici, M., Barlacchi, G., Shen, X., Cheng, W., and de Gispert, A · 2021
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Abg-coqa: Clarifying ambiguity in conversational question answering
Guo, M., Zhang, M., Reddy, S., and Alikhani, M · 2021
Cited alongside, same era.
Leveraging passage retrieval with generative models for open domain question answering
Izacard, G. and Grave, É · 2021
Cited alongside, same era.
Adaptive utterance rewriting for conversational search
Mele, I., Muntean, C. I., Nardini, F. M., Perego, R., Tonellotto, N., and Frieder, O · 2021
Cited alongside, same era.
Kilt: a benchmark for knowledge intensive language tasks
Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., et al · 2021
Cited alongside, same era.
Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance
Zhu, F., Lei, W., Huang, Y., Wang, C., Zhang, S., Lv, J., Feng, F., and Chua, T.-S · 2021
Cited alongside, same era.
Conqrr: Conversational query rewriting for retrieval with reinforcement learning
Wu, Z., Luan, Y., Rashkin, H., Reitter, D., Hajishirzi, H., Ostendorf, M., and Tomar, G. S · 2022
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Rewriting conversational utterances with instructed large language models
Galimzhanova, E., Muntean, C. I., Nardini, F. M., Perego, R., and Rocchietti, G · 2023
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Retrieval-augmented generation for large language models: A survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., and Wang, H · 2023
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Introducing bard, 2023
Google · 2023
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Raven: In-context learning with retrieval augmented encoder-decoder language models
Huang, J., Ping, W., Xu, P., Shoeybi, M., Chang, K. C.-C., and Catanzaro, B · 2023
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Topiocqa: Open-domain conversational question answering with topic switching
Adlakha, V., Dhuliawala, S., Suleman, K., de Vries, H., and Reddy, S · 2022
Cited alongside, same era.
Coqar: Question rewriting on coqa
Brabant, Q., Lecorvé, G., and Barahona, L. M. R · 2022
Cited alongside, same era.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S. S., Dai, Z., Suzgun, M., Chen, X., Chowdhery, A., Castro-Ros, A., Pellat, M., Robinson, K., Valter, D., Narang, S., Mishra, G., Yu, A., Zhao, V., Huang, Y., Dai, A., Yu, H., Petrov, S., Chi, E. H., Dean, J., Devlin, J., Roberts, A., Zhou, D., Le, Q. V., and Wei, J · 2022
Cited alongside, same era.
Dialog inpainting: Turning documents to dialogs
Dai, Z., Chaganty, A. T., Zhao, V., Amini, A., Green, M., Rashid, Q., and Guu, K · 2022
Cited alongside, same era.
Pacific: Towards proactive conversational question answering over tabular and textual data in finance
Deng, Y., Lei, W., Zhang, W., Lam, W., and Chua, T.-S · 2022
Cited alongside, same era.
Glm: General language model pretraining with autoregressive blank infilling
Du, Z., Qian, Y., Liu, X., Ding, M., Qiu, J., Yang, Z., and Tang, J · 2022
Cited alongside, same era.
Unigdd: A unified generative framework for goal-oriented document-grounded dialogue
Gao, C., Zhang, W., and Lam, W · 2022
Cited alongside, same era.
Atlas: Few-shot learning with retrieval augmented language models
Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., and Grave, E · 2023
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Openassistant conversations–democratizing large language model alignment
Köpf, A., Kilcher, Y., von Rütte, D., Anagnostidis, S., Tam, Z.-R., Stevens, K., Barhoum, A., Duc, N. M., Stanley, O., Nagyfi, R., et al · 2023
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Openassistant conversations - democratizing large language model alignment
Köpf, A., Kilcher, Y., von Rütte, D., Anagnostidis, S., Tam, Z.-R., Stevens, K., Barhoum, A., Duc, N. M., Stanley, O., Nagyfi, R., ES, S., Suri, S., Glushkov, D., Dantuluri, A., Maguire, A., Schuhmann, C., Nguyen, H., and Mattick, A · 2023
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Lost in the middle: How language models use long contexts
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P · 2023
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The flan collection: Designing data and methods for effective instruction tuning
Longpre, S., Hou, L., Vu, T., Webson, A., Chung, H. W., Tay, Y., Zhou, D., Le, Q. V., Zoph, B., Wei, J., et al · 2023
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Convgqr: Generative query reformulation for conversational search
Mo, F., Mao, K., Zhu, Y., Wu, Y., Huang, K., and Nie, J.-Y · 2023
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GPT-4, 2023
OpenAI · 2023
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Replug: Retrieval-augmented black-box language models
Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., and Yih, W.-t · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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How far can camels go? exploring the state of instruction tuning on open resources
Wang, Y., Ivison, H., Dasigi, P., Hessel, J., Khot, T., Chandu, K. R., Wadden, D., MacMillan, K., Smith, N. A., Beltagy, I., et al · 2023
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Inscit: Information-seeking conversations with mixed-initiative interactions
Wu, Z., Parish, R., Cheng, H., Min, S., Ammanabrolu, P., Ostendorf, M., and Hajishirzi, H · 2023
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Enhancing conversational search: Large language model-aided informative query rewriting
Ye, F., Fang, M., Li, S., and Yilmaz, E · 2023
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Lima: Less is more for alignment
Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., et al · 2023
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A survey on data selection for language models
Albalak, A., Elazar, Y., Xie, S. M., Longpre, S., Lambert, N., Wang, X., Muennighoff, N., Hou, B., Pan, L., Jeong, H., et al · 2024
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Introducing command r+: A scalable llm built for business, 2024
Cohere · 2024
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RA-DIT: Retrieval-augmented dual instruction tuning
Lin, X. V., Chen, X., Chen, M., Shi, W., Lomeli, M., James, R., Rodriguez, P., Kahn, J., Szilvasy, G., Lewis, M., Zettlemoyer, L., and tau Yih, W · 2024
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Introducing meta llama 3: The most capable openly available llm to date, 2024
Meta · 2024
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Instructretro: Instruction tuning post retrieval-augmented pretraining
Wang, B., Ping, W., McAfee, L., Xu, P., Li, B., Shoeybi, M., and Catanzaro, B · 2024
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RECOMP: Improving retrieval-augmented LMs with context compression and selective augmentation
Xu, F., Shi, W., and Choi, E · 2024
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