# Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs Source: [Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:11:33.256Z Authors: Phillip Schneider, Manuel Klettner, Kristiina Jokinen, Elena Simperl, Florian Matthes Published: 2024-01-03T12:28:33.000Z ## Abstract Conversational question answering systems often rely on semantic parsing to enable interactive information retrieval, which involves the generation of structured database queries from a natural language input. For information-seeking conversations about facts stored within a knowledge graph, dialogue utterances are transformed into graph queries in a process that is called knowledge-based conversational question answering. This paper evaluates the performance of large language models that have not been explicitly pre-trained on this task. Through a series of experiments on an extensive benchmark dataset, we compare models of varying sizes with different prompting techniques and identify common issue types in the generated output. Our results demonstrate that large language models are capable of generating graph queries from dialogues, with significant improvements achievable through few-shot prompting and fine-tuning techniques, especially for smaller models that exhibit lower zero-shot performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Knowledge Base Question Answering on SPICE (test)](): Knowledge Base Question Answering; SPICE (test); m-F1 (Logical Reasoning): 89.61. ## Original sources and implementation [Original paper]() [PDF]()