Papers with supervised and in-context learning approaches
FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking (2024.findings-emnlp)
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Zhuoer Wang, Leonardo Ribeiro, Alexandros Papangelis, Rohan Mukherjee, Tzu-Yen Wang, Xinyan Zhao, Arijit Biswas, James Caverlee, Angeliki Metallinou
| Challenge: | Existing supervised and in-context learning approaches suffer from high training costs, poor data efficiency, and generated API calls that can be unfaithful to the API documentation and the user’s request. |
| Approach: | They propose an output-side optimization approach that uses state-tracked constrained decoding and a lightweight model as the discriminator to rerank the beam-searched candidate generations of the large language model. |
| Outcome: | The proposed approach outperforms existing supervised and in-context learning approaches in the DSTC8 and API Bank datasets in terms of call generation accuracy, inference efficiency, and context efficiency. |