Contri(e)ve: Context + Retrieve for Scholarly Question Answering (2409.09010v1)

Abstract: Scholarly communication is a rapid growing field containing a wealth of knowledge. However, due to its unstructured and document format, it is challenging to extract useful information from them through conventional document retrieval methods. Scholarly knowledge graphs solve this problem, by representing the documents in a semantic network, providing, hidden insights, summaries and ease of accessibility through queries. Naturally, question answering for scholarly graphs expands the accessibility to a wider audience. But some of the knowledge in this domain is still presented as unstructured text, thus requiring a hybrid solution for question answering systems. In this paper, we present a two step solution using open source LLM(LLM): Llama3.1 for Scholarly-QALD dataset. Firstly, we extract the context pertaining to the question from different structured and unstructured data sources: DBLP, SemOpenAlex knowledge graphs and Wikipedia text. Secondly, we implement prompt engineering to improve the information retrieval performance of the LLM. Our approach achieved an F1 score of 40% and also observed some anomalous responses from the LLM, that are discussed in the final part of the paper.

• The paper introduces a dual-phase method integrating context extraction from structured and unstructured sources to improve scholarly Q&A.
• It employs a meticulously crafted multi-part prompt to optimize Llama3.1’s inference, balancing response accuracy with efficiency.
• Experimental results show a 40% F1 score on the Scholarly-QALD dataset, highlighting integration challenges and LLM variability.

An Essay on "Contri(e)ve: Context + Retrieve for Scholarly Question Answering"

The article outlines a dual-step approach designed to bridge the gaps in scholarly question answering systems, by leveraging the capabilities of context extraction and prompt engineering in tandem with a LLM, specifically Llama3.1. This research is notable in its attempt to hybridize the retrieval of information between structured data sources, such as scholarly knowledge graphs, and unstructured text, to improve the accessibility and usability of scholarly data.

In the first phase, context extraction is at the forefront. The authors skillfully utilize multiple data sources, including DBLP and SemOpenAlex Knowledge Graphs, alongside Wikipedia, to gather a rich tapestry of relevant information. The interconnectivity of data sources is facilitated via Uniform Resource Identifiers (URIs) and ORCID, with these identifiers serving as pivotal nodes linking various scholarly datasets. This method ensures a comprehensive retrieval of author, publication, and institutional data, maximizing the context available for question answering, yet the inconsistencies observed in ORCID integration highlight areas for potential improvement.

Secondly, the paper explores prompt engineering as an optimization strategy. Here, the authors craft a structured, multi-part prompt approach to guide the LLM's inference process effectively. It is evident that meticulous attention is given to minimizing prompt length while enhancing informational density, thus balancing response accuracy with computational efficiency. This intricate crafting of prompts underscores the uncertainties surrounding LLMs' handling of context, as demonstrated by observable discrepancies and hallucinations in response outputs.

The experiments conducted using the Scholarly-QALD dataset yield an F1 score of 40%, positioning the proposed solution as competitively effective relative to contemporary systems tackling similar hybrid problems. However, the modest score underscores the persistent challenges faced in both the seamless integration of disparate data sources and the intrinsic variability within LLM-generated responses.

Looking toward future implications, the research posits significant theoretical and practical considerations. The dual-focus on context accuracy and prompt efficiency presents a template that can be refined and adapted across diverse domains that require a fusion of structured and unstructured data for semantic extraction. The noted inconsistencies and limitations, however, stress the importance of robust testing and the need for iterative enhancements to LLMs—a reminder that such models, while sophisticated, are not impervious to the pitfalls of data noise and incomplete contexts.

In conclusion, the paper serves as an insightful contribution to the field of scholarly question answering by pushing boundaries on how LLMs engage with structured and unstructured data alike. While it innovatively harnesses the strengths of knowledge graphs and meticulously frames prompts for contextual insight, its findings also illuminate the exigent need for future work focused on stability, consistency, and data completeness. Thus, this research serves as both a milestone and a motivating call for continued exploration in AI-driven information retrieval within academic domains.