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workshop paper

ACL 2024

August 15, 2024

Bangkok, Thailand

Improving LLM-based KGQA for multi-hop Question Answering with implicit reasoning in few-shot examples

keywords:

text2sparql

sparql generation

text2cypher

cypher generation

knowledge graph question answering

llm

prompt engineering

in-context learning

multi-hop

code generation

Large language models (LLMs) have shown remarkable capabilities in generating natural language texts for various tasks. However, using LLMs for question answering on knowledge graphs still remains a challenge, especially for questions requiring multi-hop reasoning. In this paper, we present a novel planned query guidance approach that improves large language model (LLM) performance in multi-hop question answering on knowledge graphs (KGQA). We do this by designing few-shot examples that implicitly demonstrate a systematic reasoning methodology to answer multi-hop questions. We evaluate our approach for two graph query languages, Cypher and SPARQL, and show that the queries generated using our strategy outperform the queries generated using a baseline LLM and typical few-shot examples by up to 24.66\% and 7.7\% in execution match accuracy for the MetaQA and the Spider benchmarks respectively. We also conduct an ablation study to analyze the incremental effects of the different techniques of designing few-shot examples. Our results suggest that our approach enables the LLM to effectively leverage the few-shot examples to generate queries for multi-hop KGQA.

Next from ACL 2024

ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA datasets with Large Language Models
workshop paper

ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA datasets with Large Language Models

ACL 2024

+1Ronak Pradeep
Ronak Pradeep and 3 other authors

15 August 2024

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