Information Extraction from Implicit Text: Evaluating and Adapting Large Language Models

ACL ARR 2025 May Submission1183 Authors

Abstract:

Text Implicitness has always been challenging in Natural Language Processing (NLP), with traditional methods relying on explicit statements to identify entities and their relationships. From the sentence "Zuhdi attends church every Sunday", the relationship between Zuhdi and Christianity is evident for a human reader, but it presents a challenge when it must be inferred automatically.
Large language models (LLMs) have proven effective in NLP downstream tasks such as text comprehension and information extraction (IE).

This study examines how textual implicitness affects IE tasks in pre-trained LLMs: LLaMA 2.3, DeepSeekV1, and Phi1.5.
We generate two synthetic datasets of 10k implicit and explicit verbalization of biographic information to measure the impact on LLM performance and analyze whether fine-tuning implicit data improves their ability to generalize in implicit reasoning tasks.

This research presents an experiment on the internal reasoning processes of LLMs in IE, particularly in dealing with implicit and explicit contexts. The results demonstrate that fine-tuning LLM models with LoRA (low-rank adaptation) improves their performance in extracting information from implicit texts, contributing to better model interpretability and reliability.
The implementation of our study can be found at \href{https://anonymous.4open.science/r/xAi-KE-ImplicitKnowledge-C65A/}{anonymous/xAi-KE-ImplicitKnowledge}

Paper Type: Long

Research Area: Information Extraction

Research Area Keywords: open information extraction, fine-tuning, LLM/AI agents, natural language inference, textual entailment

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 1183