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Interactive Language-Based Agents.

. ETH Zurich, Zürich, Switzerland, (2023)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/613568).

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Local Saddle Point Optimization: A Curvature Exploitation Approach., , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 486-495. PMLR, (2019)Reason first, then respond: Modular Generation for Knowledge-infused Dialogue., , , , and . CoRR, (2021)How to Query Language Models?, , and . CoRR, (2021)Decoding a Neural Retriever's Latent Space for Query Suggestion., , , , , , and . EMNLP, page 8786-8804. Association for Computational Linguistics, (2022)Reason first, then respond: Modular Generation for Knowledge-infused Dialogue., , , , and . EMNLP (Findings), page 7112-7132. Association for Computational Linguistics, (2022)Boosting Search Engines with Interactive Agents., , , , , , , , , and 1 other author(s). Trans. Mach. Learn. Res., (2022)Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion., , , , , and . EMNLP (Findings), page 373-393. Association for Computational Linguistics, (2022)Interactive Language-Based Agents.. ETH Zurich, Zürich, Switzerland, (2023)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/613568).The CRINGE Loss: Learning what language not to model., , , , , and . CoRR, (2022)Boosting Search Engines with Interactive Agents., , , , , , , and . CoRR, (2021)