SEFRQO claims a self-evolving fine-tuned LLM with retrieval and execution feedback reduces query latency versus PostgreSQL, but the provided body is a different paper, blocking verification.
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering
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abstract
The widely used Fact-based Visual Question Answering (FVQA) dataset contains visually-grounded questions that require information retrieval using common sense knowledge graphs to answer. It has been observed that the original dataset is highly imbalanced and concentrated on a small portion of its associated knowledge graph. We introduce FVQA 2.0 which contains adversarial variants of test questions to address this imbalance. We show that systems trained with the original FVQA train sets can be vulnerable to adversarial samples and we demonstrate an augmentation scheme to reduce this vulnerability without human annotations.
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cs.DB 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
SEFRQO claims a self-evolving fine-tuned LLM with retrieval and execution feedback reduces query latency versus PostgreSQL, but the provided body is a different paper, blocking verification.