Hexa: Self-Improving for Knowledge-Grounded Dialogue System

A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e.g., web-search, memory retrieval) with modular approaches. However, data for such steps are often inaccessible compared to those of dialogue responses as they are unobservable in an ordinary dialogue. To fill in the absence of these data, we develop a self-improving method to improve the generative performances of intermediate steps without the ground truth data. In particular, we propose a novel bootstrapping scheme with a guided prompt and a modified loss function to enhance the diversity of appropriate self-generated responses. Through experiments on various benchmark datasets, we empirically demonstrate that our method successfully leverages a self-improving mechanism in generating intermediate and final responses and improves the performances on the task of knowledge-grounded dialogue generation. Along with the progress of Language Model (LM) pretraining, open-domain dialogue models have evolved to leverage the advantage of the transformer architecture's generalization ability (Zhang et al., 2019; Freitas et al., 2020; Roller et al., 2021; Xu et al., 2022a; Shuster et al., 2022b; Thoppilan et al., 2022). While model scaling also improves the dialogue quality (Freitas et al., 2020) as seen in large LMs, relying on sole LMs casts limitations such as hallucination and the lack of faithfulness by outdated training data (Brown et al., 2020; Thoppilan et al., 2022; Chowdhery et al., 2022). In order to overcome the limitations, prior works have adopted a modular design where multiple modules generate intermediate texts (e.g., to retrieve documents) before the final response (Lewis et al., 2020; Adolphs et al., 2021; Zhang et al., 2021; Shuster et al., 2022a). Among them, Komeili et al. (2022); Shuster et al. (2022b) have shown promising results in dialogue generation. Specifically, they adopted a modular design to integrate external knowledge (e.g., internet) and internal knowledge (e.g., memory) in dialogue models. For example, in Komeili et al. (2022), a LM first decides whether to access a knowledge in a form of text generation. Upon deciding to access knowledge, the LM generates an appropriate query for knowledge retrieval from external sources such as search engines. Then, the LM generates a response based on extracted knowledge from the accessed data. See Figure 2 of Appendix A for an illustrative example. Regarding each intermediate phase as a separate module, a convenient method of training these modules would be to apply supervised learning on each module using individual datasets (Dinan et al., 2019; Shuster et al., 2022a; Glass et al., 2022; Shuster et al., 2022b).