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 Large Language Model(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.

Open-domain question answering (Open-QA) is a common task for evaluating large language models (LLMs). However, current Open-QA evaluations are criticized for the ambiguity in questions and the lack of semantic understanding in evaluators. Complex evaluators, powered by foundation models or LLMs and pertaining to semantic equivalence, still deviate from human judgments by a large margin. We propose to study the entailment relations of answers to identify more informative and more general system answers, offering a much closer evaluation to human judgment on both NaturalQuestions and TriviaQA while being learning-free. The entailment-based evaluation we propose allows the assignment of bonus or partial marks by quantifying the inference gap between answers, enabling a nuanced ranking of answer correctness that has higher AUC than current methods.