Prompting, Retrieval, Training: An exploration of different approaches for task-oriented dialogue generation (2023)

Abstract: Task-oriented dialogue systems need to generate appropriate responses to help fulfill users’ requests. This paper explores different strategies, namely prompting, retrieval, and fine-tuning, for task-oriented dialogue generation. Through a systematic evaluation, we aim to provide valuable insights and guidelines for researchers and practitioners working on developing efficient and effective dialogue systems for real-world applications. Evaluation is performed on the MultiWOZ and Taskmaster-2 datasets, and we test various versions of FLAN-T5, GPT-3.5, and GPT-4 models. Costs associated with running these models are analyzed, and dialogue evaluation is briefly discussed. Our findings suggest that when testing data differs from the training data, fine-tuning may decrease performance, favoring a combination of a more general language model and a prompting mechanism based on retrieved examples.

## introduction task-oriented dialogue systems need to generate appropriate responses to help fulfill users' requests. recent advancements in natural language processing (nlp) have produced a shift towards leveraging large pretrained language models to tackle the generation challenge @xcite . by prompting these models with a few examples, their performance has been shown to surpass traditional approaches, eliminating the need for extensive model training @xcite . in this paper, we explore different approaches for task-oriented dialogue generation, namely through the use of prompting, retrieval mechanisms, and fine-tuning. we investigate the best strategies to leverage these approaches, considering the integration of past conversation information, the selection of appropriate retrieval methods, and the assessment of the benefits of fine-tuning @xcite @xcite . during our investigation, we assessed various state-of-the-art instruction-based models, including different size versions of @xcite , gpt-3.5, and gpt-4, provided by openai @xcite . these models, known for their impressive language generation capabilities, serve as the foundation for our experiments, through which we tested different strategies. we evaluate the performance of these models on widely used benchmark datasets, namely multi-woz and taskmaster-2, which offer diverse and challenging dialogue scenarios @xcite . additionally, we analyze the computational costs associated with running the models, considering the trade-off between performance and resource requirements. moreover, we discuss dialogue system evaluation, addressing the metrics and criteria that best capture the quality and effectiveness of task-oriented dialogue generation @xcite . the main contributions of this paper are foot_0 : ## related work task-oriented dialogue generation has garnered significant attention, leading to a wide range of research efforts. recent studies have focused on the use of large pretrained language models for dialogue systems. @xcite introduced gpt, a transformer @xcite using generative pretraining, which demonstrated impressive performance in various nlp tasks. subsequent research explored the benefits of fine-tuning pretrained models specifically for dialogue generation tasks. for instance, @xcite proposed mintl, a system that fine-tuned a pretrained model on task-oriented data and established new state-ofthe-art results. similarly, @xcite employed fine-tuning on a larger pretrained model of approximately 37 b parameters and used around 1.56 t words of public dialogue data and web text, improving in all metrics. prompting has emerged as a valuable technique for improving the performance of pretrained language models. it involves providing specific examples or instructions as input to guide the generation process. @xcite demonstrated the effectiveness of prompts when using language models to generate coherent and contextually appropriate responses. a recent work by @xcite addresses prompting in the context of dialogue systems, showing how instruction tuning may benefit certain test tasks. retrieval-enhanced methods have also been extensively explored in dialogue systems. @xcite integrated text retrieval and text generation models to build a hybrid conversational system that outperformed retrieval-based and generation-based approaches. in addition, several studies have also incorporated retrieval mechanisms in combination with generative models to enhance dialogue system performance @xcite @xcite . while the aforementioned studies have made substantial contributions to the field, this paper aims to expand upon the existing literature by thoroughly investigating the integration of prompting, retrieval mechanisms, and fine-tuning in taskoriented dialogue generation. specifically, we explore the efficacy of these approaches and analyze their impact on system performance, considering both the quality of generated responses and the computational costs associated with running the models. furthermore, as far as we know, we are the first work employing the gpt-3.5 and gpt-4 models for the multiwoz and taskmaster-2 datasets, establishing baselines for each. ## method in our main approach, we propose to use a dense retrieval model that, given a dialogue, will retrieve other similar dialogues. we then use their answers to generate a new answer using a transformer. figure 1 illustrates how our system can be used for inference, depicting its components. ## experimental setup broadly, our experiments consisted in testing different generation models on the task of answer generation in task-oriented dialogues. in some cases, this also involved the use of information retrieval mechanisms or fine-tuning models. ## conclusions we performed a systematic evaluation of different ways of using state-of-the-art retrieval and generation models for task-oriented answer generation. we experimented with dense retrieval models, flan-t5, gpt-3.5, and gpt-4, evaluating them on the multiwoz and taskmaster-2 datasets. having explored these models separately and combined, we concluded that retrieving possible answers greatly improved the generated responses in terms of automatic metrics. moreover, if training data is available and it does not differ much from the data seen during inference, then fine-tuning the generation model can greatly improve its performance, surpassing strong results from large language models such as gpt-3.5 and gpt-4. if the dialogue system is to be used in a context of high variability, then using a more general large language model and only fine-tuning the retrieval component can be a better procedure. in future work, we shall test the generation model with other prompts and evaluate how the performance is affected. moreover, we plan to improve the training of the retrieval model, since it can be integrated with any generation system and, as we have shown, significantly improve its performance. training with different datasets can improve its generalization ability, and strategies like maximal marginal relevance @xcite can be used to collect diverse answers. lastly, active retrieval augmentation methods similar to flare @xcite can also be employed. this involves generating initial answers from the context (without retrieval), refining the retrieval query with these generated answers, and, lastly, re-generating the final answer with the retrieved candidates.