Synergizing LLMs and Knowledge Graphs: A Novel Approach to Software Repository-Related Question Answering

• URL: http://arxiv.org/abs/2412.03815v1
• Date: Thu, 05 Dec 2024 02:18:03 GMT
• Title: Synergizing LLMs and Knowledge Graphs: A Novel Approach to Software Repository-Related Question Answering
• Authors: Samuel Abedu, SayedHassan Khatoonabadi, Emad Shihab,
• Abstract summary: Software repositories contain valuable information for gaining insights into their development process.<n> extracting insights from these repository data is time-consuming and requires technical expertise.<n>This study aims to improve the accuracy of LLM-based chatbots in answering repository-related questions by augmenting them with knowledge graphs.
• Score: 3.076436880934678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software repositories contain valuable information for gaining insights into their development process. However, extracting insights from these repository data is time-consuming and requires technical expertise. While software engineering chatbots have been developed to facilitate natural language interactions with repositories, they struggle with understanding natural language and accurately retrieving relevant data. This study aims to improve the accuracy of LLM-based chatbots in answering repository-related questions by augmenting them with knowledge graphs. We achieve this in a two-step approach; (1) constructing a knowledge graph from the repository data and (2) synergizing the knowledge graph with LLM to allow for the natural language questions and answers. We curated a set of 20 questions with different complexities and evaluated our approach on five popular open-source projects. Our approach achieved an accuracy of 65%. We further investigated the limitations and identified six key issues, with the majority relating to the reasoning capability of the LLM. We experimented with a few-shot chain-of-thought prompting to determine if it could enhance our approach. This technique improved the overall accuracy to 84%. Our findings demonstrate the synergy between LLMs and knowledge graphs as a viable solution for making repository data accessible to both technical and non-technical stakeholders.

Related papers

• Enhancing Repository-Level Software Repair via Repository-Aware Knowledge Graphs [8.467850621024672]
  Repository-level software repair faces challenges in bridging semantic gaps between issue descriptions and code patches. Existing approaches, which mostly depend on large language models (LLMs), suffer from semantic ambiguities, limited structural context understanding, and insufficient reasoning capability. We propose a novel repository-aware knowledge graph (KG) that accurately links repository artifacts (issues and pull requests) and entities.
  arXiv  Detail & Related papers  (2025-03-27T17:21:47Z)
• Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation [81.18701211912779]
  We introduce an Adaptive Multi-Aspect Retrieval-augmented over KGs (Amar) framework. This method retrieves knowledge including entities, relations, and subgraphs, and converts each piece of retrieved text into prompt embeddings. Our method has achieved state-of-the-art performance on two common datasets.
  arXiv  Detail & Related papers  (2024-12-24T16:38:04Z)
• How to Understand Whole Software Repository? [64.19431011897515]
  An excellent understanding of the whole repository will be the critical path to Automatic Software Engineering (ASE) We develop a novel method named RepoUnderstander by guiding agents to comprehensively understand the whole repositories. To better utilize the repository-level knowledge, we guide the agents to summarize, analyze, and plan.
  arXiv  Detail & Related papers  (2024-06-03T15:20:06Z)
• Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts [50.06633829833144]
  Large Language Models (LLMs) are effective in performing various NLP tasks, but struggle to handle tasks that require extensive, real-world knowledge. We propose a benchmark that requires knowledge of long-tail facts for answering the involved questions. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required.
  arXiv  Detail & Related papers  (2024-05-10T15:10:20Z)
• Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT [2.8000537365271367]
  Large language models (LLMs) have emerged as a vibrant research topic. LLMs face challenges in remembering events, incorporating new information, and addressing domain-specific issues or hallucinations. This article proposes a method for automatically constructing a Knowledge Graph from multiple data sources.
  arXiv  Detail & Related papers  (2024-04-14T16:34:31Z)
• Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context [4.1229332722825]
  This paper introduces a novel framework that combines graph-driven context retrieval in conjunction to knowledge graphs based enhancement. We conduct experiments on various Large Language Models (LLMs) with different parameter sizes to evaluate their ability to ground knowledge and determine factual accuracy in answers to open-ended questions. Our methodology GraphContextGen consistently outperforms dominant text-based retrieval systems, demonstrating its robustness and adaptability to a larger number of use cases.
  arXiv  Detail & Related papers  (2024-01-23T11:25:34Z)
• RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge [69.79676144482792]
  This study aims to evaluate the ability of LLMs to distinguish reliable information from external knowledge. Our benchmark consists of two tasks, Question Answering and Text Generation, and for each task, we provide models with a context containing counterfactual information.
  arXiv  Detail & Related papers  (2023-11-14T13:24:19Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators [78.63553017938911]
  Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks. However, community concerns abound regarding the factuality and potential implications of using this uncensored knowledge. We introduce CONNER, designed to evaluate generated knowledge from six important perspectives.
  arXiv  Detail & Related papers  (2023-10-11T08:22:37Z)
• Self-Knowledge Guided Retrieval Augmentation for Large Language Models [59.771098292611846]
  Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. Self-Knowledge guided Retrieval augmentation (SKR) is a simple yet effective method which can let LLMs refer to the questions they have previously encountered.
  arXiv  Detail & Related papers  (2023-10-08T04:22:33Z)
• Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering [7.888547093390469]
  Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks. We propose to augment the knowledge directly in the input of LLMs. Our framework, Knowledge-Augmented language model PromptING (KAPING), requires no model training, thus completely zero-shot.
  arXiv  Detail & Related papers  (2023-06-07T04:15:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.