keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM

Large language models (LLMs) [1-5] have recently become the new darling of academia and industry due to their remarkable performance in a variety of natural language processing (NLP) tasks. With the blessing of techniques such as large-scale pre-training [6], instruction tuning [7], and reinforcement learning from human feedback (RLHF) [8, 9], existing pretrained LLMs have demonstrated unique capabilities in language understanding, generation, interaction, and reasoning. These powerful capabilities of LLMs also drive many emergent research topics (e.g., instruction learning [10], in-context learning [1], chain-of-thought prompting [11], etc.) to further investigate their huge potentials, and bring unlimited possibilities for humans to build advanced artificial intelligence systems. However, alongside these advancements, a pressing issue that plagues LLMs has been widely criticized as "hallucination", referred to as a phenomenon where LLMs tend to generate text that is incorrect, nonsensical, or not real [12]. To alleviate the phenomenon of "hallucination" during the generation of LLMs, a promising direction is to retrieve the factual knowledge that are highly relevant to the user query, and then guide LLMs to generate response according to the retrieved context, resulting in retrieval-augmented LMs [13, 14] that have recently demonstrated strong performance in knowledge intensive tasks, especially for knowledge-based question answering (KBQA). The workflow of existing retrieval-augmented LMs [15, 16] mainly relies on embedding-based retrieval methods, which will first encode various forms of knowledge base and also the user query into the same latent space, then use a semantic similarity metric to retrieve the top-K most relevant documents as prompt, and finally instruct LLMs to only use the provided context to answer the user query.