Self-GIVE: Associative Thinking from Limited Structured Knowledge for Enhanced Large Language Model Reasoning
Journal:
arXiv
Published Date:
May 21, 2025
Abstract
When addressing complex questions that require new information, people often
associate the question with existing knowledge to derive a sensible answer. For
instance, when evaluating whether melatonin aids insomnia, one might associate
"hormones helping mental disorders" with "melatonin being a hormone and
insomnia a mental disorder" to complete the reasoning. Large Language Models
(LLMs) also require such associative thinking, particularly in resolving
scientific inquiries when retrieved knowledge is insufficient and does not
directly answer the question. Graph Inspired Veracity Extrapolation (GIVE)
addresses this by using a knowledge graph (KG) to extrapolate structured
knowledge. However, it involves the construction and pruning of many
hypothetical triplets, which limits efficiency and generalizability. We propose
Self-GIVE, a retrieve-RL framework that enhances LLMs with automatic
associative thinking through reinforcement learning. Self-GIVE extracts
structured information and entity sets to assist the model in linking to the
queried concepts. We address GIVE's key limitations: (1) extensive LLM calls
and token overhead for knowledge extrapolation, (2) difficulty in deploying on
smaller LLMs (3B or 7B) due to complex instructions, and (3) inaccurate
knowledge from LLM pruning. Specifically, after fine-tuning using self-GIVE
with a 135 node UMLS KG, it improves the performance of the Qwen2.5 3B and 7B
models by up to $\textbf{28.5%$\rightarrow$71.4%}$ and
$\textbf{78.6$\rightarrow$90.5%}$ in samples $\textbf{unseen}$ in challenging
biomedical QA tasks. In particular, Self-GIVE allows the 7B model to match or
outperform GPT3.5 turbo with GIVE, while cutting token usage by over 90\%.
Self-GIVE enhances the scalable integration of structured retrieval and
reasoning with associative thinking.
