LLM2KB: Constructing Knowledge Bases using instruction tuned context
  aware Large Language Models
Anmol Nayak Hari Prasad Timmapathini
The advent of Large Language Models (LLM) has revolutionized the field of
natural language processing, enabling significant progress in various
applications. One key area of interest is the construction of Knowledge Bases
(KB) using these powerful models. Knowledge bases serve as repositories of
structured information, facilitating information retrieval and inference tasks.
Our paper proposes LLM2KB, a system for constructing knowledge bases using
large language models, with a focus on the Llama 2 architecture and the
Wikipedia dataset. We perform parameter efficient instruction tuning for
Llama-2-13b-chat and StableBeluga-13B by training small injection models that
have only 0.05 % of the parameters of the base models using the Low Rank
Adaptation (LoRA) technique. These injection models have been trained with
prompts that are engineered to utilize Wikipedia page contexts of subject
entities fetched using a Dense Passage Retrieval (DPR) algorithm, to answer
relevant object entities for a given subject entity and relation. Our best
performing model achieved an average F1 score of 0.6185 across 21 relations in
the LM-KBC challenge held at the ISWC 2023 conference.