CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs (2023.emnlp-main)
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Taha Aksu, Devamanyu Hazarika, Shikib Mehri, Seokhwan Kim, Dilek Hakkani-Tur, Yang Liu, Mahdi Namazifar
| Challenge: | Instruction-based multitasking has played a critical role in the success of large language models (LLMs) when exposed to complex instructions with multiple constraints, they lag against state-of-the-art models like ChatGPT. |
| Approach: | They propose a framework that unifies a large number of dialog tasks in the same format and allows programmatic induction of complex instructions without manual effort. |
| Outcome: | The proposed framework unifies a large number of dialog tasks in the same format and allows programmatic induction of complex instructions without manual effort. |
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