An LLM plus a repository knowledge graph answers software repository questions with 84% accuracy when few-shot chain-of-thought prompting is added, outperforming an intent-based bot and web-search GPT-4o.
Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions
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abstract
Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is the user perspective. In particular, there remain many open questions regarding how best to address the diverse information needs of users, incorporating varying facets and levels of difficulty. This paper introduces a taxonomy of user information needs, which guides us to study the pros, cons and possible synergies of Large Language Models, Knowledge Graphs and Search Engines. From this study, we derive a roadmap for future research.
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Synergizing LLMs and Knowledge Graphs: A Novel Approach to Software Repository-Related Question Answering
An LLM plus a repository knowledge graph answers software repository questions with 84% accuracy when few-shot chain-of-thought prompting is added, outperforming an intent-based bot and web-search GPT-4o.