TL;DR
Evaluations on standard KBQA benchmarks show that the proposed ARI-KBQA enhances model performance with a reduced search space, especially in complex multi-hop query scenarios.
GCA-KBQA is proposed, a step-wise fine-tuned LLM-based framework that employs hop-wise generation, knowledge-assisted calibration, and path-level assembly to construct complete LFs for KBQA, demonstrating its superior performance compared to state-of-the-art baselines.
The Intelligent Prompt Construction Framework (IPCF), which equips an autonomous agent with the ability to dynamically generate task-specific prompts, and achieves performance gains over existing baselines on the OK-VQA and A-OKVQA datasets.
Knowledge-based Visual Question Answering aims to answer questions about an image by integrating external knowledge with visual and textual information. Recent approaches often rely on in-context learning to prompt Large Language Models (LLMs) with multimodal context in a zero-shot or few-shot manner. However, we obser...
EKS is a novel framework that leverages entity relations in commonsense knowledge graphs to dynamically generate knowledge sentences relevant to both visual and textual entities and formulates knowledge selection as a relevance scoring problem, where semantic similarity is used to measure the relevance between knowledg...
KGCaRe is proposed, a hybrid approach that combines neural retrieval with symbolic reasoning over LLM-generated KGs that consistently outperforms existing baselines, including Vanilla LLM, Code Prompt, Text Prompt, Think-on-Graph, Vanilla RAG, and HybridContextQA.
This work proposes HybridRAG-BN, a retrieval-augmented framework for Bangla KBQA that integrates hybrid retrieval using BM25 and BGE-M3, answer generation using the GGUF version of Gemma-4-31B-Instruct, and a LoRA-fine-tuned Gemma-4-31B-Instruct model for answer verification and refinement.
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