Neon: News Entity-Interaction Extraction for Enhanced Question Answering (2411.12449v2)

Abstract: Capturing fresh information in near real-time and using it to augment existing 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 However, the information modeled by the parametric memory of LLMs is often outdated, and Web results from prototypical retrieval systems may fail to capture the latest relevant information and struggle to handle conflicting reports in evolving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.

• The paper introduces a novel framework that extracts temporal entity interactions from news to enhance LLM-based question answering.
• It employs openIE and two variants, Neon(M1) and Neon(M2), to construct a dynamic, entity-centric knowledge graph from real-time news data.
• Experimental evaluations on over 3000 queries demonstrate significant improvements in relevance and accuracy of temporal responses.

Overview of the Neon Framework for Temporal Question Answering

The paper "Neon: News Entity-Interaction Extraction for Enhanced Question Answering" presents an innovative framework aimed at addressing the challenges associated with temporal question answering (QA) using LLMs. With the rise of LLMs as powerful tools for various informational tasks, their application in dynamic domains such as news poses significant challenges due to the outdated nature of parametric memory and the inadequacies of current information retrieval systems. Neon proposes a robust solution by constructing a time-stamped, entity-centric knowledge graph from news streams, allowing for the augmentation of LLMs with up-to-date contextual information to improve the accuracy and relevancy of their responses.

Problem Context and Motivation

The main impetus for this research arises from the necessity of capturing emerging information in real-time contexts. Traditional LLMs, while trained on vast datasets, fail to integrate the temporal shifts and developments occurring within news ecosystems, leading to potential inaccuracies and outdated knowledge in the responses generated for user queries. This shortcoming is particularly significant in scenarios where users seek information on current events, requiring rapid assimilation of the latest details involving entities. The authors identify these gaps in traditional systems and underscore the importance of integrating temporal information to enhance the utility of LLMs in answering entity-centric questions, especially in rapidly evolving fields.

Methodology

The Neon framework follows a structured approach to build an enhanced QA system. The authors outline a process to identify and extract emerging interactions between entities from news articles using open Information Extraction (openIE) methodologies. The framework constructs a knowledge graph where nodes are entities, and edges capture interactions such as events or activities. Two variants of this framework, Neon($\mathcal{M_1}$) and Neon($\mathcal{M_2}$), are proposed to optimize the extraction process.

1. Neon($\mathcal{M_1$): This variant generates entity-interactions by identifying surrounding objects from text chunks based on a predefined set of target subjects. The integration of entity marking during the chunk retrieval and extraction improves context-specific interaction modeling.
2. Neon($\mathcal{M_2$): This approach leverages co-occurrences of subject and object entities in news texts. It is structured to handle batch retrieval of chunks, optimizing resource use, and enhancing the extraction of implicit interactions between entity pairs over time.

Further, the paper outlines practical retrieval strategies for temporal QA, adopting a dense indexing mechanism and integrating temporal indexes for effective retrieval as required by user queries. Emphasizing generative models, the authors leverage LLMs to evaluate and generate responses, underscored by their recalled Neon tuples.

Experimental Evaluation

The authors conduct comprehensive experiments evaluating Neon against various temporal and generic retrieval benchmarks. Specifically, they utilize a dataset of over 3000 real-world queries spanning multiple entities and assess the efficacy of augmented LLM responses through both automated and human evaluations. Notably, Neon shows significant enhancements in relevance and faithfulness of generated responses, particularly in aligning with entities' temporal dynamics. The paper convincingly demonstrates that the structured incorporation of temporal news tuples through Neon raises the performance of LLMs in QA tasks over time-dependent datasets.

Implications and Future Directions

The Neon framework underscores a positive shift towards integrating rich, time-sensitive data into LLM-based systems, offering a nuanced approach for news-driven query answering amidst ever-changing information landscapes. Although the results emphasize the effectiveness of a time-stamped knowledge graph approach within contemporary news environments, the paper suggests potential areas for improvement. Further exploration could entail scaling the system to accommodate more diverse data streams or investigating more advanced interaction extraction methodologies that refine precision and real-time processing capabilities.

In conclusion, the Neon paper provides a distinct contribution to the field by addressing critical challenges within temporal QA via entity-interaction extraction from news articles. This work sets a foundation for more temporally aware LLMs and paves the way for future research that might broaden applications to other dynamic domains such as social media and market analysis.