Large Language Models as Versatile Dialogue Assistants: Enabling Zero-shot Dialogue State Tracking through Function Calling

Kernekoncepter

Large language models can be effectively leveraged for zero-shot dialogue state tracking by treating dialogue domains as functions and integrating function calling capabilities into the language model's output.

Resumé

This paper introduces a novel approach called FNCTOD that enables large language models (LLMs) to perform zero-shot dialogue state tracking (DST) by treating each dialogue domain as a distinct function and integrating function calling capabilities into the language model's output.
The key highlights of the approach are:

Redefining DST as function calling: Each dialogue domain is modeled as a unique function, with the associated slot values serving as arguments. This allows the language model to generate function calls along with its responses.

Prompt-based function call generation: The domain schemas are converted into function specifications and incorporated into the system prompt. This guides the model to first select the appropriate function and then generate the corresponding arguments.

In-context prompting: To enhance the model's ability to generate correct function call formats, the system prompt includes demonstration examples of function calls.

Fine-tuning on diverse dialogues: A 13B parameter LLAMA2-CHAT model is fine-tuned on a small collection of 7,200 dialogues across 36 diverse domains, equipping it with function calling capabilities while preserving its response generation abilities.

The experimental results on the MultiWOZ benchmark demonstrate the effectiveness of the FNCTOD approach. It enables modestly-sized open-source LLMs (7-13B parameters) to surpass the previous state-of-the-art achieved by advanced proprietary models like ChatGPT. Additionally, FNCTOD improves ChatGPT's performance, beating the previous individual best results for GPT-3.5 and GPT-4 by 4.8% and 14%, respectively.
The fine-tuned FNCTOD-LLAMA2-13B model also matches the zero-shot DST performance of ChatGPT, bridging the gap between open-source and proprietary models. This work demonstrates the potential of integrating function calling capabilities into LLMs to handle both general conversations and task-oriented dialogues in diverse domains.

Statistik

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Citater

"Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts."
"However, their effectiveness in task-oriented dialogues (TOD), which requires not only response generation but also effective dialogue state tracking (DST) within specific tasks and domains, remains less satisfying."
"Our experimental results demonstrate that our approach achieves exceptional performance with both modestly sized open-source and also proprietary LLMs: with in-context prompting it enables various 7B or 13B parameter models to surpass the previous state-of-the-art (SOTA) achieved by ChatGPT, and improves ChatGPT's performance beating the SOTA by 5.6% average joint goal accuracy (JGA)."

Vigtigste indsigter udtrukket fra

Large Language Models as Zero-shot Dialogue State Tracker through Function Calling

by Zekun Li,Zhi... kl. arxiv.org 05-02-2024

https://arxiv.org/pdf/2402.10466.pdf

Dybere Forespørgsler

How can the FNCTOD approach be extended to handle other dialogue tasks beyond DST, such as response generation and task completion?

The FNCTOD approach can be extended to handle other dialogue tasks beyond DST by incorporating function calling for response generation and task completion. Here's how it can be done:

Response Generation:

Similar to how DST is treated as function calling, response generation can also be framed as a function call. Each response type or template can be considered a function, with the necessary arguments being the context or input from the user.
The system prompt can include specifications for different response functions, guiding the model on how to generate appropriate responses based on the dialogue context.
In the dialogue prompt, the model can generate the function call for the appropriate response function along with the necessary arguments, ensuring coherent and contextually relevant responses.

Task Completion:

Task completion in a dialogue system involves taking actions or providing information to fulfill user requests or tasks.
Task-specific functions can be defined for different types of tasks, such as booking a reservation, providing information, or scheduling appointments.
The FNCTOD approach can guide the model to identify the task-specific function to call based on the user's request and generate the necessary arguments to complete the task effectively.

By extending the FNCTOD approach to encompass response generation and task completion through function calling, the model can seamlessly transition between different dialogue tasks, providing a more comprehensive and integrated conversational experience for users.

How can the potential limitations of the function calling paradigm be addressed, and how can it be further improved to handle more complex or open-ended dialogue scenarios?

The function calling paradigm, while effective for DST and certain dialogue tasks, may have limitations when applied to more complex or open-ended dialogue scenarios. Here are some ways to address these limitations and enhance the function calling approach:

Contextual Understanding:

Enhance the model's ability to understand and interpret context-rich dialogue by incorporating contextual embeddings or memory mechanisms to capture long-term dependencies and nuances in the conversation.
Implement hierarchical function calling, where functions can be called at different levels of abstraction to handle complex dialogue structures and multi-turn interactions.

Dynamic Function Generation:

Introduce dynamic function generation mechanisms that allow the model to create new functions on-the-fly based on user inputs or dialogue context, enabling adaptability to novel scenarios and tasks.
Incorporate meta-learning techniques to enable the model to learn how to generate new functions or adapt existing functions for diverse dialogue scenarios.

Multi-Task Learning:

Extend the function calling paradigm to support multi-task learning, where the model can handle multiple dialogue tasks simultaneously by invoking different functions for each task and managing the interactions between them.
Implement reinforcement learning strategies to optimize function calling decisions in real-time, considering task priorities, user preferences, and system constraints.

By addressing these aspects and further refining the function calling paradigm with advanced techniques and strategies, the approach can be enhanced to handle more complex and open-ended dialogue scenarios effectively.

Given the advancements in language models, how can the evaluation of task-oriented dialogue systems be improved to better reflect real-world deployment scenarios and user experiences?

To improve the evaluation of task-oriented dialogue systems and better reflect real-world deployment scenarios and user experiences, the following strategies can be implemented:

Human Evaluation:

Conduct extensive human evaluations to assess the system's performance in terms of naturalness, coherence, and task completion accuracy.
Engage real users in interactive sessions to gather feedback on system usability, satisfaction, and overall user experience.

Simulation in Realistic Environments:

Simulate real-world deployment scenarios by integrating the dialogue system into virtual environments or chat platforms where users can interact with the system in a more authentic setting.
Collect data from these simulations to evaluate system performance under diverse conditions and user behaviors.

Long-Term User Studies:

Conduct longitudinal studies to observe how users interact with the dialogue system over an extended period, capturing user adaptation, system improvements, and evolving user needs.
Analyze user feedback, system logs, and performance metrics to iteratively enhance the system based on real-world usage patterns.

Domain-Specific Evaluation Metrics:

Develop domain-specific evaluation metrics that align with the objectives and requirements of different task-oriented dialogue applications.
Consider metrics like task success rate, user satisfaction scores, task completion time, and system robustness in handling diverse user inputs.

Ethical and Bias Considerations:

Incorporate ethical considerations and bias detection mechanisms in the evaluation process to ensure fair and unbiased interactions with users from diverse backgrounds.
Implement transparency and explainability features to enable users to understand how the system operates and makes decisions.

By integrating these strategies into the evaluation process of task-oriented dialogue systems, researchers and developers can obtain more comprehensive insights into system performance, usability, and user satisfaction in real-world deployment scenarios.