Built on

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518
2015
Earlier work this paper cites.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
Original
2017
Earlier work this paper cites.
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., Weston, J.: Wizard of wikipedia: Knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241 (2018)
Original
2018
Earlier work this paper cites.
Ghazvininejad, M., Brockett, C., Chang, M.W., Dolan, B., Gao, J., Yih, W.t., Galley, M.: A knowledge-grounded neural conversation model. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 32 (2018)
2018
Earlier work this paper cites.
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training. OpenAI Blog (2018)
2018
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
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Engstrom, L., Ilyas, A., Santurkar, S., Tsipras, D., Janoos, F., Rudolph, L., Madry, A.: Implementation matters in deep policy gradients: A case study on ppo and trpo. arXiv preprint arXiv:2005.12729 (2020)
Original
2020
Earlier work this paper cites.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.t., Rocktäschel, T., et al.: Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021)
Original
2021
Earlier work this paper cites.
Lu, Y., Bartolo, M., Moore, A., Riedel, S., Stenetorp, P.: Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. arXiv preprint arXiv:2104.08786 (2021)
Original
2021
Earlier work this paper cites.

Similar

PENG, B.: RWKV-LM (Aug 2021). https://doi.org/10.5281/zenodo.5196577, https://github.com/BlinkDL/RWKV-LM
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Thorne, J., Yazdani, M., Saeidi, M., Silvestri, F., Riedel, S., Halevy, A.: Database reasoning over text. arXiv preprint arXiv:2106.01074 (2021)
Original
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Thorne, J., Yazdani, M., Saeidi, M., Silvestri, F., Riedel, S., Halevy, A.: From natural language processing to neural databases. In: Proceedings of the VLDB Endowment. vol. 14, pp. 1033–1039. VLDB Endowment (2021)
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2022
Cited alongside, same era.

Then

Zamani, H., Diaz, F., Dehghani, M., Metzler, D., Bendersky, M.: Retrieval-enhanced machine learning. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 2875–2886 (2022)
2022
Later among the works it cites.
Zhang, Y., Sun, S., Gao, X., Fang, Y., Brockett, C., Galley, M., Gao, J., Dolan, B.: Retgen: A joint framework for retrieval and grounded text generation modeling. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 11739–11747 (2022)
Original
2022
Later among the works it cites.
Bacciu, A., Trappolini, G., Santilli, A., Rodolà, E., Silvestri, F.: Fauno: The italian large language model that will leave you senza parole! arXiv preprint arXiv:2306.14457 (2023)
Original
2023
Closest in time.
Liu, N.F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., Liang, P.: Lost in the middle: How language models use long contexts. arXiv preprint arXiv:2307.03172 (2023)
Original
2023

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2023

RRAML: Reinforced Retrieval Augmented Machine Learning

Bacciu, Andrea, Cuconasu, Florin, Siciliano, Federico et al.

Understand

The emergence of large language models (LLMs) has revolutionized machine learning and related fields, showcasing remarkable abilities in comprehending, generating, and manipulating human language.

However, their conventional usage through API-based text prompt submissions imposes certain limitations in terms of context constraints and external source availability.
To address these challenges, we propose a novel framework called Reinforced Retrieval Augmented Machine Learning (RRAML).
RRAML integrates the reasoning capabilities of LLMs with supporting information retrieved by a purpose-built retriever from a vast user-provided database.

Built on

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. nature 518
2015
Earlier work this paper cites.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
Original
2017
Earlier work this paper cites.
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., Weston, J.: Wizard of wikipedia: Knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241 (2018)
Original
2018
Earlier work this paper cites.
Ghazvininejad, M., Brockett, C., Chang, M.W., Dolan, B., Gao, J., Yih, W.t., Galley, M.: A knowledge-grounded neural conversation model. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 32 (2018)
2018
Earlier work this paper cites.
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training. OpenAI Blog (2018)
2018
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Engstrom, L., Ilyas, A., Santurkar, S., Tsipras, D., Janoos, F., Rudolph, L., Madry, A.: Implementation matters in deep policy gradients: A case study on ppo and trpo. arXiv preprint arXiv:2005.12729 (2020)
Original
2020
Earlier work this paper cites.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.t., Rocktäschel, T., et al.: Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021)
Original
2021
Earlier work this paper cites.
Lu, Y., Bartolo, M., Moore, A., Riedel, S., Stenetorp, P.: Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. arXiv preprint arXiv:2104.08786 (2021)
Original
2021
Earlier work this paper cites.

Similar

PENG, B.: RWKV-LM (Aug 2021). https://doi.org/10.5281/zenodo.5196577, https://github.com/BlinkDL/RWKV-LM
2021
Cited alongside, same era.
Thorne, J., Yazdani, M., Saeidi, M., Silvestri, F., Riedel, S., Halevy, A.: Database reasoning over text. arXiv preprint arXiv:2106.01074 (2021)
Original
2021
Cited alongside, same era.
Thorne, J., Yazdani, M., Saeidi, M., Silvestri, F., Riedel, S., Halevy, A.: From natural language processing to neural databases. In: Proceedings of the VLDB Endowment. vol. 14, pp. 1033–1039. VLDB Endowment (2021)
2021
Cited alongside, same era.
Carmel, D., Cohen, N., Ingber, A., Kravi, E.: Ir evaluation and learning in the presence of forbidden documents. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 556–566 (2022)
2022
Cited alongside, same era.

Then

Zamani, H., Diaz, F., Dehghani, M., Metzler, D., Bendersky, M.: Retrieval-enhanced machine learning. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 2875–2886 (2022)
2022
Later among the works it cites.
Zhang, Y., Sun, S., Gao, X., Fang, Y., Brockett, C., Galley, M., Gao, J., Dolan, B.: Retgen: A joint framework for retrieval and grounded text generation modeling. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 11739–11747 (2022)
Original
2022
Later among the works it cites.
Bacciu, A., Trappolini, G., Santilli, A., Rodolà, E., Silvestri, F.: Fauno: The italian large language model that will leave you senza parole! arXiv preprint arXiv:2306.14457 (2023)
Original
2023
Closest in time.
Liu, N.F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., Liang, P.: Lost in the middle: How language models use long contexts. arXiv preprint arXiv:2307.03172 (2023)
Original
2023

Beyond the bibliography

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Open on alphaXiv

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Nie, F., Chen, M., Zhang, Z., Cheng, X.: Improving few-shot performance of language models via nearest neighbor calibration. arXiv preprint arXiv:2212.02216 (2022)

Original

2022

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OpenAI: Chatgpt: A large-scale language model for conversational ai. OpenAI Blog (November 2022)

2022

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Sauchuk, A., Thorne, J., Halevy, A., Tonellotto, N., Silvestri, F.: On the role of relevance in natural language processing tasks. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 1785–1789 (2022)

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Sauchuk, A., Thorne, J., Halevy, A.Y., Tonellotto, N., Silvestri, F.: On the role of relevance in natural language processing tasks. In: Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J.S., Kazai, G. (eds.) SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022. pp. 1785–1789. ACM (2022). https://doi.org/10.1145/3477495.3532034, https://doi.org/10.1145/3477495.3532034

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Cited alongside, same era.

Scao, T.L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A.S., Yvon, F., Gallé, M., et al.: Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100 (2022)

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2022

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OpenAI: Gpt-4 technical report (2023)

2023

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Peng, B., Galley, M., He, P., Cheng, H., Xie, Y., Hu, Y., Huang, Q., Liden, L., Yu, Z., Chen, W., et al.: Check your facts and try again: Improving large language models with external knowledge and automated feedback. arXiv preprint arXiv:2302.12813 (2023)

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Closest in time.

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G.: Llama: Open and efficient foundation language models (2023)

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Closest in time.

Trappolini, G., Santilli, A., Rodolà, E., Halevy, A., Silvestri, F.: Multimodal neural databases. arXiv preprint arXiv:2305.01447 (2023)

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Xie, Z., Singh, S., McAuley, J., Majumder, B.P.: Factual and informative review generation for explainable recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 13816–13824 (2023)

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