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Chaining language model (LM) calls as composable modules is fueling a new way of programming, but ensuring LMs adhere to important constraints requires heuristic "prompt engineering".
Natural semantics
Kahn, G · 1987
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Jass - java with assertions
Bartetzko, D., Fischer, C., Möller, M., and Wehrheim, H · 2001
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Lexically constrained decoding for sequence generation using grid beam search
Hokamp, C. and Liu, Q · 2017
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., and Manning, C. D · 2018
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Improved lexically constrained decoding for translation and monolingual rewriting
Hu, J. E., Khayrallah, H., Culkin, R., Xia, P., Chen, T., Post, M., and Van Durme, B · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Model assertions for monitoring and improving ml models
Kang, D., Raghavan, D., Bailis, P., and Zaharia, M · 2020
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Baleen: Robust multi-hop reasoning at scale via condensed retrieval
Khattab, O., Potts, C., and Zaharia, M · 2021
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Colbertv2: Effective and efficient retrieval via lightweight late interaction
Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., and Zaharia, M · 2021
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LangChain, October 2022
Chase, H · 2022
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Khattab, O., Santhanam, K., Li, X. L., Hall, D., Liang, P., Potts, C., and Zaharia, M · 2022
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Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Trivedi, H., Balasubramanian, N., Khot, T., and Sabharwal, A · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Cited alongside, same era.
Qiu, L., Jiang, L., Lu, X., Sclar, M., Pyatkin, V., Bhagavatula, C., Wang, B., Kim, Y., Choi, Y., Dziri, N., et al · 2023
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Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails, 2023
Rebedea, T., Dinu, R., Sreedhar, M., Parisien, C., and Cohen, J · 2023
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Reflexion: Language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. R., and Yao, S · 2023
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The art of llm refinement: Ask, refine, and trust
Shridhar, K., Sinha, K., Cohen, A., Wang, T., Yu, P., Pasunuru, R., Sachan, M., Weston, J., and Celikyilmaz, A · 2023
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Prompting is programming: A query language for large language models
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Python Software Foundation · 2023
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Tyen, G., Mansoor, H., Chen, P., Mak, T., and Cărbune, V · 2023
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Dspy: Compiling declarative language model calls into self-improving pipelines
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