clem:todd: A Framework for the Systematic Benchmarking of LLM-Based Task-Oriented Dialogue System Realisations (2505.05445v1)

Abstract: The emergence of instruction-tuned LLMs has advanced the field of dialogue systems, enabling both realistic user simulations and robust multi-turn conversational agents. However, existing research often evaluates these components in isolation-either focusing on a single user simulator or a specific system design-limiting the generalisability of insights across architectures and configurations. In this work, we propose clem todd (chat-optimized LLMs for task-oriented dialogue systems development), a flexible framework for systematically evaluating dialogue systems under consistent conditions. clem todd enables detailed benchmarking across combinations of user simulators and dialogue systems, whether existing models from literature or newly developed ones. It supports plug-and-play integration and ensures uniform datasets, evaluation metrics, and computational constraints. We showcase clem todd's flexibility by re-evaluating existing task-oriented dialogue systems within this unified setup and integrating three newly proposed dialogue systems into the same evaluation pipeline. Our results provide actionable insights into how architecture, scale, and prompting strategies affect dialogue performance, offering practical guidance for building efficient and effective conversational AI systems.

A Framework for Systematic Benchmarking of Task-Oriented Dialogue Systems

The paper "clem:todd: A Framework for the Systematic Benchmarking of LLM-Based Task-Oriented Dialogue System Realizations" presents a structured approach to evaluating task-oriented dialogue systems (TODs) using instruction-tuned LLMs. Dialogue systems are pivotal in enabling conversational agents to assist users in completing specific tasks through natural language interactions. These systems have evolved significantly with the introduction of LLMs, which facilitate robust user simulations and multi-turn conversations. However, current evaluations in the field suffer from inconsistencies regarding datasets, metrics, and computational settings, limiting the comparability and generalizability of findings across different dialogue system architectures.

This paper introduces clem:todd, a flexible framework aimed at overcoming these evaluation challenges by providing a consistent setup for benchmarking dialogue systems. Clem:todd is built on a self-play paradigm where each dialogue interaction is modeled as a two-player game between a user simulator powered by an LLM and a dialogue system. This setup is managed by a central controller, ensuring standardized evaluation metrics and resource constraints are applied.

Key Contributions

1. Unified Evaluation Framework: The paper proposes and implements clem:todd, offering an integrated setup for evaluating TOD systems using both existing dialogue architectures and newly developed ones. It relies on interchangeable components for user simulation and dialogue delivery, accommodating various models from the literature while allowing room for new instances.
2. Insights into Dialogue System Design: By benchmarking multiple architectures, clem:todd provides insights into how different configurations (monolithic, modular, etc.) impact dialogue performance. It assesses trade-offs in computational costs and efficiency, offering practical guidance for developing effective conversational AI systems.
3. Robustness and Adaptability: Clem:todd is not restricted to a particular dataset or domain. The authors demonstrate how the framework can adapt to new domains, including synthetic data configurations and unrealistic scenarios, to test the resilience and generalization of dialogue systems.

Results and Analysis

The comparative paper conducted using clem:todd reveals significant trends and findings:

• Model Size and Task Performance: Larger models, such as GPT-4o and Qwen2.5-32B, achieve higher task success rates across various dialogue tasks, especially in monolithic setups. Smaller models often produced invalid outputs due to format violations.
• Monolithic vs. Modular Systems: While monolithic systems offer lower computational costs, modular architectures, particularly those using LLMs for dynamic module management, present valuable trade-offs between flexibility and computational efficiency. The Modular-LLM approach, in particular, strikes a favorable balance.
• Influence of User Simulator Quality: The quality of user simulations significantly affects dialogue system performance, with robust simulations enhancing evaluation reliability. The paper introduced the us-spread metric to quantitatively reflect this impact on system robustness.

Implications and Future Directions

The implications of this research extend across both practical applications and theoretical exploration in AI. The clem:todd framework informs developers about optimal dialogue system designs relative to desired outcomes and resource constraints. Furthermore, it emphasizes the importance of robust user simulation in achieving reliable evaluations and model comparisons.

Future work will likely build upon clem:todd's foundations to explore multi-agent dialogue scenarios or further elaborate on the modular design of dialogue systems to capture more complex interaction dynamics. Enhancing the diversity of simulated dialogues could also augment the robustness and adaptability of TOD evaluations. Addressing the limitations noted by the authors, including the strict response format adherence and the need for more closed-weight models, could further refine dialogue system benchmarking. Overall, clem:todd represents a significant step towards achieving more standardized and insightful evaluations in the development of task-oriented dialogue systems.