Information Extraction (IE) is crucial for converting unstructured data into
structured formats like Knowledge Graphs (KGs). A key task within IE is
Relation Extraction (RE), which identifies relationships between entities in
text. Various RE methods exist, including supervised, unsupervised, weakly
supervised, and rule-based approaches. Recent studies leveraging pre-trained
language models (PLMs) have shown significant success in this area. In the
current era dominated by Large Language Models (LLMs), fine-tuning these models
can overcome limitations associated with zero-shot LLM prompting-based RE
methods, especially regarding domain adaptation challenges and identifying
implicit relations between entities in sentences. These implicit relations,
which cannot be easily extracted from a sentence's dependency tree, require
logical inference for accurate identification. This work explores the
performance of fine-tuned LLMs and their integration into the Retrieval
Augmented-based (RAG) RE approach to address the challenges of identifying
implicit relations at the sentence level, particularly when LLMs act as
generators within the RAG framework. Empirical evaluations on the TACRED,
TACRED-Revisited (TACREV), Re-TACRED, and SemEVAL datasets show significant
performance improvements with fine-tuned LLMs, including Llama2-7B, Mistral-7B,
and T5 (Large). Notably, our approach achieves substantial gains on SemEVAL,
where implicit relations are common, surpassing previous results on this
dataset. Additionally, our method outperforms previous works on TACRED, TACREV,
and Re-TACRED, demonstrating exceptional performance across diverse evaluation
scenarios.

Fine-tuned large language models integrated into the Retrieval-Augmented-based approach demonstrate exceptional performance in identifying implicit relations, surpassing previous results on SemEVAL and achieving substantial gains on TACRED, TACREV, and Re-TACRED datasets.