Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language
  Models by Learning from Knowledge Graphs
Large Language Models (LLMs) have exhibited impressive proficiency in various
natural language processing (NLP) tasks, which involve increasingly complex
reasoning. Knowledge reasoning, a primary type of reasoning, aims at deriving
new knowledge from existing one.While it has been widely studied in the context
of knowledge graphs (KGs), knowledge reasoning in LLMs remains underexplored.
In this paper, we introduce Chain-of-Knowledge, a comprehensive framework for
knowledge reasoning, including methodologies for both dataset construction and
model learning. For dataset construction, we create KnowReason via rule mining
on KGs. For model learning, we observe rule overfitting induced by naive
training. Hence, we enhance CoK with a trial-and-error mechanism that simulates
the human process of internal knowledge exploration. We conduct extensive
experiments with KnowReason. Our results show the effectiveness of CoK in
refining LLMs in not only knowledge reasoning, but also general reasoning
benchmarkms.
66839b7801c30ad93e224f2f
Yifei Zhang
admin_assigned
2024-07-03T15:13:55.321Z
643c047326f177a3e41627b6
/avatars/ade75cebd049daf080ba80a80d516240.svg
Yifei Zhang
user
amstrongzyf
66839b7801c30ad93e224f30
Xintao Wang
admin_assigned
2025-02-19T22:41:16.717Z
64c7bf2c4524c2aea7eac0b3
https://cdn-avatars.huggingface.co/v1/production/uploads/64c7bf2c4524c2aea7eac0b3/5ocZ69MvN4RFv86Aa7ks3.png
Xintao Wang
user
Neph0s
66839b7801c30ad93e224f31
Jiaqing Liang
admin_assigned
2024-07-03T15:14:11.988Z
634014022c72fd4ca251fed7
/avatars/32a85d41f7435f7facfd580d9d124c33.svg
liangjiaqing
user
liangjiaqing
66839b7801c30ad93e224f32
Sirui Xia
66839b7801c30ad93e224f33
Lida Chen
admin_assigned
2024-07-03T15:14:38.897Z
63ef522ae2319f3531891451
/avatars/9b6ea11a1b61cd96b6ac30a86e9b5329.svg
chenlida
user
fogdog
66839b7801c30ad93e224f34
Yanghua Xiao
Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language
  Models by Learning from Knowledge Graphs
Large Language Models (LLMs) have exhibited impressive proficiency in various
natural language processing (NLP) tasks, which involve increasingly complex
reasoning. Knowledge reasoning, a primary type of reasoning, aims at deriving
new knowledge from existing one.While it has been widely studied in the context
of knowledge graphs (KGs), knowledge reasoning in LLMs remains underexplored.
In this paper, we introduce Chain-of-Knowledge, a comprehensive framework for
knowledge reasoning, including methodologies for both dataset construction and
model learning. For dataset construction, we create KnowReason via rule mining
on KGs. For model learning, we observe rule overfitting induced by naive
training. Hence, we enhance CoK with a trial-and-error mechanism that simulates
the human process of internal knowledge exploration. We conduct extensive
experiments with KnowReason. Our results show the effectiveness of CoK in
refining LLMs in not only knowledge reasoning, but also general reasoning
benchmarkms.
Chain-of-Knowledge enhances Large Language Models with a rule-based framework and trial-and-error mechanism to improve knowledge and general reasoning tasks.
Large Language Models knowledge reasoning knowledge graphs Chain-of-Knowledge KnowReason rule mining rule overfitting trial-and-error mechanism