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. |
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| Challenge: | Recent research attention in task-oriented dialogue systems focuses on end-to-end neural models. |
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Sungryull Sohn, Yiwei Lyu, Anthony Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee
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| Challenge: | Existing approaches for task-oriented dialogue systems rely on a unified schema across domains, but we propose a schema-aware model for task oriented dialogues based on 'slots' |
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| Challenge: | Task-oriented dialogue systems are designed to be composed of several functional modules, but lacks a general-purpose instruction-following language model. |
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| Challenge: | Recent task-oriented dialogue systems are trained on annotated dialogues, but when domain knowledge changes, the initial model may become obsolete. |
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| Challenge: | Recent work on end-to-end dialogue models with pre-trained dialogue corpora shows promising performance in the conversational system. |
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| Challenge: | Recent studies have shown that Large Language Models perform insufficiently as TOD systems. |
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| Challenge: | Existing language models pre-trained on general text overlook the one-to-many property of task-oriented dialogues, where multiple responses can be appropriate given the same context. |
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| Challenge: | Task-oriented dialog systems require external knowledge base to generate a response . current systems require scanning the KB at each turn, which is inefficient when the kb scales up . |
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