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ESAinsTOD: A Unified End-to-End Schema-Aware Instruction-Tuning Framework for Task-Oriented Dialog Modeling

arXiv:2603.09691v1 Announce Type: new Abstract: Existing end-to-end modeling methods for modular task-oriented dialog systems are typically tailored to specific datasets, making it challenging to adapt to new dialog scenarios. In this work, we propose ESAinsTOD, a unified End-to-end Schema-Aware Instruction-tuning framework for general Task-Oriented Dialog modeling. This framework introduces a structured methodology to go beyond simply fine-tuning Large Language Models (LLMs), enabling flexible adaptation to various dialogue task flows and schemas. Specifically, we leverage full-parameter fine-tuning of LLMs and introduce two alignment mechanisms to make the resulting system both instruction-aware and schema-aware: (i) instruction alignment, which ensures that the system faithfully follows task instructions to complete various task flows from heterogeneous TOD datasets; and (ii) schema alignment, which encourages the system to make predictions adhering to the specified schema. In addi

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Dechuan Teng, Chunlin Lu, Libo Qin, Wanxiang Che · · 1 min read · 10 views
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arXiv:2603.09691v1 Announce Type: new Abstract: Existing end-to-end modeling methods for modular task-oriented dialog systems are typically tailored to specific datasets, making it challenging to adapt to new dialog scenarios. In this work, we propose ESAinsTOD, a unified End-to-end Schema-Aware Instruction-tuning framework for general Task-Oriented Dialog modeling. This framework introduces a structured methodology to go beyond simply fine-tuning Large Language Models (LLMs), enabling flexible adaptation to various dialogue task flows and schemas. Specifically, we leverage full-parameter fine-tuning of LLMs and introduce two alignment mechanisms to make the resulting system both instruction-aware and schema-aware: (i) instruction alignment, which ensures that the system faithfully follows task instructions to complete various task flows from heterogeneous TOD datasets; and (ii) schema alignment, which encourages the system to make predictions adhering to the specified schema. In addition, we employ session-level end-to-end modeling, which allows the system to access the results of previously executed task flows within the dialogue history, to bridge the gap between the instruction-tuning paradigm and the real-world application of TOD systems. Empirical results show that while a fine-tuned LLM serves as a strong baseline, our structured approach provides significant additional benefits. In particular, our findings indicate that: (i) ESAinsTOD outperforms state-of-the-art models by a significant margin on end-to-end task-oriented dialog modeling benchmarks: CamRest676, In-Car and MultiWOZ; (ii) more importantly, it exhibits superior generalization capabilities across various low-resource settings, with the proposed alignment mechanisms significantly enhancing zero-shot performance; and (iii) our instruction-tuning paradigm substantially improves the model's robustness against data noise and cascading errors.

Executive Summary

This article proposes ESAinsTOD, a unified end-to-end schema-aware instruction-tuning framework for task-oriented dialog modeling. The framework introduces a structured methodology to fine-tune large language models, enabling flexible adaptation to various dialogue task flows and schemas. The authors leverage full-parameter fine-tuning, instruction alignment, and schema alignment to make the system both instruction-aware and schema-aware. Empirical results show that ESAinsTOD outperforms state-of-the-art models on end-to-end task-oriented dialog modeling benchmarks and exhibits superior generalization capabilities across various low-resource settings. The proposed framework substantially improves the model's robustness against data noise and cascading errors. The findings have significant implications for the development of more effective and robust task-oriented dialog systems.

Key Points

  • ▸ ESAinsTOD is a unified end-to-end schema-aware instruction-tuning framework for task-oriented dialog modeling.
  • ▸ The framework introduces a structured methodology to fine-tune large language models.
  • ▸ ESAinsTOD leverages full-parameter fine-tuning, instruction alignment, and schema alignment to make the system both instruction-aware and schema-aware.

Merits

Strength in Methodology

The proposed framework introduces a structured methodology to fine-tune large language models, enabling flexible adaptation to various dialogue task flows and schemas.

Improved Generalization

ESAinsTOD exhibits superior generalization capabilities across various low-resource settings.

Demerits

Limited Evaluation

The evaluation of ESAinsTOD is limited to a specific set of benchmarks and datasets.

Expert Commentary

The proposed framework, ESAinsTOD, is a significant contribution to the field of task-oriented dialog systems. The authors' use of a structured methodology to fine-tune large language models is a key strength of the framework. However, the evaluation of ESAinsTOD is limited to a specific set of benchmarks and datasets, and it is unclear whether the framework would generalize to other domains. Additionally, the proposed framework relies on the availability of large language models, which may not be feasible for all applications. Despite these limitations, the findings have significant implications for the development of more effective and robust task-oriented dialog systems.

Recommendations

  • ✓ Future research should evaluate ESAinsTOD on a broader range of benchmarks and datasets to assess its generalizability.
  • ✓ The authors should investigate the use of ESAinsTOD in other domains and applications to assess its feasibility and effectiveness.

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