SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph

• URL: http://arxiv.org/abs/2402.04627v1
• Date: Wed, 7 Feb 2024 07:24:01 GMT
• Title: SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph
• Abstract summary: We evaluate strategies for fine-tuning the OpenLlama LLM for question answering over life science knowledge graphs. We propose an end-to-end data augmentation approach for extending a set of existing queries over a given knowledge graph. We also investigate the role of semantic "clues" in the queries, such as meaningful variable names and inline comments.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The recent success of Large Language Models (LLM) in a wide range of Natural Language Processing applications opens the path towards novel Question Answering Systems over Knowledge Graphs leveraging LLMs. However, one of the main obstacles preventing their implementation is the scarcity of training data for the task of translating questions into corresponding SPARQL queries, particularly in the case of domain-specific KGs. To overcome this challenge, in this study, we evaluate several strategies for fine-tuning the OpenLlama LLM for question answering over life science knowledge graphs. In particular, we propose an end-to-end data augmentation approach for extending a set of existing queries over a given knowledge graph towards a larger dataset of semantically enriched question-to-SPARQL query pairs, enabling fine-tuning even for datasets where these pairs are scarce. In this context, we also investigate the role of semantic "clues" in the queries, such as meaningful variable names and inline comments. Finally, we evaluate our approach over the real-world Bgee gene expression knowledge graph and we show that semantic clues can improve model performance by up to 33% compared to a baseline with random variable names and no comments included.

Related papers

• MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA [5.340503366346383]
  Large language models (LLMs) have demonstrated strong reasoning performance, but their tendency to hallucinate limits their reliability in knowledge-intensive tasks.<n>We propose MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning.
  arXiv  Detail & Related papers  (2026-07-16T04:40:05Z)
• ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering [54.72902502486611]
  ReAG is a Reasoning-Augmented Multimodal RAG approach that combines coarse- and fine-grained retrieval with a critic model that filters irrelevant passages.<n>ReAG significantly outperforms prior methods, improving answer accuracy and providing interpretable reasoning grounded in retrieved evidence.
  arXiv  Detail & Related papers  (2025-11-27T19:01:02Z)
• Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts [6.14987671066697]
  In this paper, we propose Tagging-Augmented Generation (TAG) as a lightweight data augmentation strategy for long-context scenarios.<n>We validate our hypothesis by augmenting two challenging and directly relevant question-answering benchmarks -- NoLima and NovelQA.<n>We show that tagging the context or even just adding tag definitions into QA prompts leads to consistent performance gains over the baseline.
  arXiv  Detail & Related papers  (2025-10-27T03:23:25Z)
• Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching [61.824094419641575]
  Large Language Models (LLMs) struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA)<n>We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures.<n>Existing methods usually employ resource-intensive, non-scalable reasoning on vanilla KGs, but overlook this gap.<n>We propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries.
  arXiv  Detail & Related papers  (2025-09-25T06:48:52Z)
• GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models [59.72897499248909]
  We propose a novel graph retriever trained end-to-end with Large Language Models (LLMs)<n>Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together.<n>Our approach consistently achieves state-of-the-art performance, validating the strength of joint graph-LLM optimization for complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-09-20T02:38:00Z)
• SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection [81.78173888579941]
  Large Language Models (LLMs) are considered a well-suited method to increase the quality of the question-answering functionality.<n>LLMs are trained on web data, where researchers have no control over whether the benchmark or the knowledge graph was already included in the training data.<n>This paper introduces a novel method that evaluates the quality of LLMs by generating a SPARQL query from a natural-language question.
  arXiv  Detail & Related papers  (2025-07-18T12:28:08Z)
• Decompositional Reasoning for Graph Retrieval with Large Language Models [1.034893617526558]
  Large Language Models (LLMs) excel at many NLP tasks, but struggle with multi-hop reasoning and factual consistency.<n>We propose a novel retrieval approach that integrates textual knowledge graphs into the LLM reasoning process via query decomposition.<n>Our method decomposes complex questions into sub-questions, retrieves relevant textual subgraphs, and composes a question-specific knowledge graph to guide answer generation.
  arXiv  Detail & Related papers  (2025-06-16T11:44:28Z)
• GenKI: Enhancing Open-Domain Question Answering with Knowledge Integration and Controllable Generation in Large Language Models [75.25348392263676]
  Open-domain question answering (OpenQA) represents a cornerstone in natural language processing (NLP)<n>We propose a novel framework named GenKI, which aims to improve the OpenQA performance by exploring Knowledge Integration and controllable Generation.
  arXiv  Detail & Related papers  (2025-05-26T08:18:33Z)
• Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models [51.47994645529258]
  We propose Question-Aware Knowledge Graph Prompting (QAP), which incorporates question embeddings into GNN aggregation to dynamically assess KG relevance. Experimental results demonstrate that QAP outperforms state-of-the-art methods across multiple datasets, highlighting its effectiveness.
  arXiv  Detail & Related papers  (2025-03-30T17:09:11Z)
• 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)
• Assessing SPARQL capabilities of Large Language Models [0.0]
  We focus on measuring out-of-the box capabilities of Large Language Models to work with SPARQL. We implement benchmarking tasks in the LLM-KG-Bench framework for automated execution and evaluation. Our findings indicate that working with SPARQL SELECT queries is still challenging for LLMs.
  arXiv  Detail & Related papers  (2024-09-09T08:29:39Z)
• Integrating Large Language Models with Graph-based Reasoning for Conversational Question Answering [58.17090503446995]
  We focus on a conversational question answering task which combines the challenges of understanding questions in context and reasoning over evidence gathered from heterogeneous sources like text, knowledge graphs, tables, and infoboxes. Our method utilizes a graph structured representation to aggregate information about a question and its context.
  arXiv  Detail & Related papers  (2024-06-14T13:28:03Z)
• Optimizing Language Model's Reasoning Abilities with Weak Supervision [48.60598455782159]
  We present textscPuzzleBen, a weakly supervised benchmark that comprises 25,147 complex questions, answers, and human-generated rationales. A unique aspect of our dataset is the inclusion of 10,000 unannotated questions, enabling us to explore utilizing fewer supersized data to boost LLMs' inference capabilities.
  arXiv  Detail & Related papers  (2024-05-07T07:39:15Z)
• CuriousLLM: Elevating Multi-Document Question Answering with LLM-Enhanced Knowledge Graph Reasoning [0.9295048974480845]
  We propose CuriousLLM, an enhancement that integrates a curiosity-driven reasoning mechanism into an LLM agent. This mechanism enables the agent to generate relevant follow-up questions, thereby guiding the information retrieval process more efficiently. Our experiments show that CuriousLLM significantly boosts LLM performance in multi-document question answering (MD-QA)
  arXiv  Detail & Related papers  (2024-04-13T20:43:46Z)
• LaSagnA: Language-based Segmentation Assistant for Complex Queries [39.620806493454616]
  Large Language Models for Vision (vLLMs) generate detailed perceptual outcomes, including bounding boxes and masks. In this study, we acknowledge that the main cause of these problems is the insufficient complexity of training queries. We present three novel strategies to effectively handle the challenges arising from the direct integration of the proposed format.
  arXiv  Detail & Related papers  (2024-04-12T14:40:45Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• 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)
• 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)
• An In-Context Schema Understanding Method for Knowledge Base Question Answering [70.87993081445127]
  Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details. We propose a simple In-Context Understanding (ICSU) method that enables LLMs to directly understand schemas by leveraging in-context learning.
  arXiv  Detail & Related papers  (2023-10-22T04:19:17Z)
• Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering [9.158919909909146]
  In this paper, we propose a novel way of employing labeled data such as it informs LLM of some undesired output. Experiments on three multi-span question answering datasets and a keyphrase extraction dataset show that our new prompting strategy consistently improves LLM's in-context learning performance.
  arXiv  Detail & Related papers  (2023-06-07T15:20:24Z)
• Exploiting Abstract Meaning Representation for Open-Domain Question Answering [18.027908933572203]
  We utilize Abstract Meaning Representation (AMR) graphs to assist the model in understanding complex semantic information. Results from Natural Questions (NQ) and TriviaQA (TQ) demonstrate that our GST method can significantly improve performance.
  arXiv  Detail & Related papers  (2023-05-26T16:00:16Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)

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.