Well Begun is Half Done: Generator-agnostic Knowledge Pre-Selection for Knowledge-Grounded Dialogue (2023)

Abstract: Accurate knowledge selection is critical in knowledge-grounded dialogue systems. Towards a closer look at it, we offer a novel perspective to organize existing literature, i.e., knowledge selection coupled with, after, and before generation. We focus on the third under-explored category of study, which can not only select knowledge accurately in advance, but has the advantage to reduce the learning, adjustment, and interpretation burden of subsequent response generation models, especially LLMs. We propose \tt{GATE} , a generator-agnostic knowledge selection method, to prepare knowledge for subsequent response generation models by selecting context-related knowledge among different knowledge structures and variable knowledge requirements. Experimental results demonstrate the superiority of \tt{GATE} , and indicate that knowledge selection before generation is a lightweight yet effective way to facilitate LLMs (e.g., ChatGPT) to generate more informative responses.

## introduction knowledge-grounded dialogue systems generate informative responses by incorporating external knowledge, such as unstructured documents and structured knowledge graphs @xcite . this generation process requires an agent to select context-related knowledge to support high user engagement. taking figure 1 (a) as an example, the knowledge "mark boal wrote zero dark thirty" contributes to a high-quality response compared with the knowledge "zero dark thirty has genre war film", when the user focuses on "who wrote zero dark thirty". many efforts have been devoted to selecting context-relevant knowledge, and we classify them into three categories based on the occasion when knowledge selection is performed. the first category is co-selection @xcite @xcite wherein knowledge selection and response generation are executed in a coupled manner (c.f. figure 1 (b)-➀ ). although this category of approach is efficient and has been extensively researched, it is costly to learn and is difficult to interpret and adjust when errors arise in the generated responses. the second category is post-selection @xcite , namely, the knowledge is selected after the generation and is used to correct the knowledge error in the generated response (c.f. figure 1 (b)-➁ ). this category is skilled in adjusting local errors yet has minimal impact on enhancing the informativeness of the response. surprisingly, the third category, pre-selection, has been under-explored by previous research. this category performs knowledge selection as an independent model before response generation (c.f. figure 1 (b)-➂ ). in this paper, we pay attention to this study, not only because of the overlook by current work but also because selecting knowledge accurately in advance can provide the potential to reduce the learning, adjustment, and interpretation burden of subsequent response generation models, especially for large language models (llms). to select knowledge for preparation, in addition to the accuracy required for knowledge selection, there are two key issues that need to be addressed: to resolve the above issues, we propose gate, a generator-agnostic knowledge selection method to prepare knowledge for subsequent various response generation models, e.g., bart @xcite and t5 @xcite . gate has the ability to confront both unstructured and structured knowledge and to adapt the number of desired knowledge according to different dialogue contexts. specifically, gate consists of: knowledge structure unifying, knowledge scoring, and knowledge pool size adapting module. further, we employ a reinforcement learning (rl) framework to train gate, optimizing the reward of selecting appropriate knowledge in both quality and quantity. the experimental results on two datasets demonstrate the superiority of gate on knowledge selection, and that gate can facilitate the response generation model (including chatgpt) to generate more informative responses. we believe that gate provides a lightweight and efficient solution, which is a potentially viable way for reducing the learning, adjustment, and interpretation burden of llms. in summary, the main contributions of this work are as follows. in contrast, gate is "plugand-play" and can enhance response generation for various generation models. knowledge selection after generation this knowledge selection category denotes that knowledge selection is executed as an independent model after response generation, as shown in figure 1 (b)-➁. post-selection is dedicated to correcting potential knowledge errors in the response. dziri et al. (2021) address hallucinations in responses by replacing them with correct knowledge obtained from the knowledge graph. xue et al. (2022) employ text infilling to incorporate retrieved knowledge into incomplete responses. however, these methods would diminish the fluency and naturalness of responses, whereas gate does not compromise generation models. knowledge selection before generation this knowledge selection category denotes that knowledge selection is executed as an independent model before response generation, as shown in figure 1 (b)-➂. pre-selection is dedicated to improving the accuracy of knowledge selection and further enhancing the quality of response generation. a few works have actually employed pre-selection without emphasizing or providing a formal definition. jung et al. (2020) implement graph-based reasoning through attention flows to select knowledge (i.e., paths in the graph). eric et al. (2021) collected an augmented dataset and proposed a ranking-generation pipeline to evaluate it. li et al. (2022) constructs semantic graphs of textual knowledge and performs selection based on node similarity. yang et al. (2022) proposed a topic-shift aware knowledge selector to utilize the role-initiative information to help select knowledge. however, the above methods are designed to address specific knowledge types, while gate can select knowledge across different knowledge bases. in summary, existing methods are constrained to specific knowledge types or generation models, significantly limiting the generalization ability, and rely on a fixed-size knowledge pool, which could undermine the performance of response @xcite . in contrast, gate operates in a pre-selection category that can handle diverse knowledge types and adapt the knowledge pool size to enhance various generation models. ## experiments we conduct experiments in knowledge selection and response generation to investigate the effectiveness of knowledge pre-selection in gate. we also analyze the auxiliary capability of knowledge pre-selection on the generation model and the advantages of gate adaptive knowledge pool size.