Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources
arXiv (Cornell University)(2023)
摘要
We present chain-of-knowledge (CoK), a novel framework that augments large
language models (LLMs) by dynamically incorporating grounding information from
heterogeneous sources. It results in more factual rationales and reduced
hallucination in generation. Specifically, CoK consists of three stages:
reasoning preparation, dynamic knowledge adapting, and answer consolidation.
Given a knowledge-intensive question, CoK first prepares several preliminary
rationales and answers while identifying the relevant knowledge domains. If
there is no majority consensus among the answers from samples, CoK corrects the
rationales step by step by adapting knowledge from the identified domains.
These corrected rationales can plausibly serve as a better foundation for the
final answer consolidation. Unlike prior studies that primarily use
unstructured data, CoK also leverages structured knowledge sources such as
Wikidata and tables that provide more reliable factual information. To access
both unstructured and structured knowledge sources in the dynamic knowledge
adapting stage, we propose an adaptive query generator that allows the
generation of queries for various types of query languages, including SPARQL,
SQL, and natural sentences. Moreover, to minimize error propagation between
rationales, CoK corrects the rationales progressively using preceding corrected
rationales to generate and correct subsequent rationales. Extensive experiments
show that CoK consistently improves the performance of LLMs on
knowledge-intensive tasks across different domains.
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关键词
large language model,knowledge grounding
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