Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation (2022)

Abstract: Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to leverage these large models as knowledge bases for downstream tasks. In this work, we answer the aforementioned question in unsupervised knowledge-grounded conversation. We explore various methods that best elicit knowledge from large models. Our human study indicates that, though hallucinations exist, large models post the unique advantage of being able to output common sense and summarize facts that cannot be directly retrieved from the search engine. To better exploit such generated knowledge in dialogue generation, we treat the generated knowledge as a noisy knowledge source and propose the posterior-based reweighing as well as the noisy training strategy. Empirical results on two benchmarks show advantages over the state-of-the-art methods.

## introduction knowledge-grounded conversation @xcite is the task where the model could reply to a dialogue history based on extra knowledge. compared to standard conversational modeling, this extra knowledge prevents the model from generating generic and noninformative responses @xcite . typically, at each turn of the conversation, a pool of knowledge candidates will be retrieved from a knowledge base like unstructured documents (e.g., wikipedia) @xcite or a structured knowledge graph @xcite . the model then learns to select the most related knowledge from this pool, in an unsupervised manner, to generate its response. however, constructing and maintaining knowledge bases are time-consuming and expensive. recent studies have shown that large pre-trained mod-els are capable of grasping knowledge from unsupervised text corpora and memorizing facts to their weights @xcite @xcite @xcite . these large models can even perform reasoning implicitly @xcite . in light of this remarkable capacity of large models, we explore the possibility of leveraging large models as a new knowledge source for unsupervised knowledge-grounded conversation. in this work, we first investigate the quality of knowledge generated by large models. we examine which tuning method, including the conventional fine-tuning @xcite and the recently proposed prefix-tuning @xcite , best prompts knowledge from large models for a given dialogue history. we then design a human evaluation protocol and conduct an extensive quality assessment of the generated knowledge. despite some extent of hallucinations (plausible statements with factual errors) persist @xcite , large models can mostly generate related and correct knowledge for the future development of dialogue. moreover, some of this knowledge is not simply paraphrased or copied from web pages: they summarize scattered facts on the internet (see section 5.1). these observations advocate the unique value of employing large models as knowledge bases. owing to the hallucinations, it is risky to put generated knowledge directly into the dialogue system as the misinformation could contaminate the response. we instead consider the generated knowledge as a noisy knowledge source and use it to aid the knowledge selection process. specifically, we measure its similarity to each knowledge candidate and refine the knowledge selection accordingly (see section 3.2). we further estimate the posterior of the refined knowledge selection distribution, inspired by the fact that the posterior detangles the one-to-many relation between dialogue context and @xcite and prefix-tuning @xcite . ## eliciting knowledge from large models in this section, we first introduce the tuning methods and large pre-trained models we used to generate knowledge for a given dialogue history. then we show the tagset developed for evaluating the generated knowledge. ## exploiting generated knowledge for conversation in this section, we first review the state-of-theart approach -plato-kag @xcite . then we develop our method on top of plato-kag to exploit generated knowledge. ## conclusion in this work, we show that large pre-trained models could serve as knowledge bases for unsupervised knowledge-grounded conversation. the study on the generated knowledge of large models has the following observations: