Papers with dialogue success rate
Unsupervised End-to-End Task-Oriented Dialogue with LLMs: The Power of the Noisy Channel (2024.emnlp-main)
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| Challenge: | a task-oriented dialogue system requires turn-level annotations for interacting with their APIs. |
| Approach: | They propose an unsupervised approach that infers turn-level annotations as latent variables using a noisy channel model to build an end-to-end dialogue agent. |
| Outcome: | The proposed method doubles the success rate of a strong GPT-3.5 benchmark. |