Neon: News Entity-Interaction Extraction for Enhanced Question Answering

TL;DR

NEON is a framework that extracts real-time entity interactions from news to build timestamped knowledge graphs, significantly improving question answering accuracy for evolving news topics.

Contribution

The paper introduces NEON, a novel approach integrating openIE tuples into LLMs for real-time news entity interaction extraction and enhanced temporal QA.

Findings

Improved QA performance on news-related queries.

Effective extraction of emerging entity interactions.

Enhanced temporal relevance in LLM responses.

Abstract

Capturing fresh information in near real-time and using it to augment existing large language models (LLMs) is essential to generate up-to-date, grounded, and reliable output. This problem becomes particularly challenging when LLMs are used for informational tasks in rapidly evolving fields, such as Web search related to recent or unfolding events involving entities, where generating temporally relevant responses requires access to up-to-the-hour news sources. However, the information modeled by the parametric memory of LLMs is often outdated, and Web results from prototypical retrieval systems may fail to capture the latest relevant information and struggle to handle conflicting reports in evolving news. To address this challenge, we present the NEON framework, designed to extract emerging entity interactions -- such as events or activities -- as described in news articles. NEON constructs an entity-centric timestamped knowledge graph that captures such interactions, thereby facilitating enhanced QA capabilities related to news events. Our framework innovates by integrating open Information Extraction (openIE) style tuples into LLMs to enable in-context retrieval-augmented generation. This integration demonstrates substantial improvements in QA performance when tackling temporal, entity-centric search queries. Through NEON, LLMs can deliver more accurate, reliable, and up-to-date responses.