CounterCurate: Enhancing Visio-Linguistic Reasoning with Counterfactual Examples

Grunnleggende konsepter

CounterCurate enhances visio-linguistic reasoning by addressing physically grounded reasoning and leveraging text and image generation models for semantic counterfactual fine-tuning.

Sammendrag

Abstract:

• CounterCurate proposes a framework to improve visio-linguistic compositional reasoning.
• Identifies under-explored problems in physically grounded reasoning and semantic counterfactual fine-tuning.
• Demonstrates significant performance improvements on benchmarks.

Introduction:

• Large language models show remarkable knowledge but lack in compositional reasoning.
• Multimodal models leverage image-text pairs but struggle with compositional reasoning.
• Current research focuses on evaluation benchmarks or rule-based counterfactual fine-tuning.

Data Extraction:

• "Our approach shows significant improvements such as 33% for CLIP and 37% for LLaVA on our Flickr30k-Positions benchmark."
• "Our method empirically demonstrates a significant performance boost by fine-tuning CLIP and LLaVA using our data generation pipeline on benchmarks such as SugarCrepe."

Statistikk

私たちのアプローチは、Flickr30k-PositionsベンチマークでCLIPに33％、LLaVAに37％という大幅な性能向上を示しています。
私たちの方法は、SugarCrepeなどのベンチマークでCLIPとLLaVAをファインチューニングすることで、明らかなパフォーマンス向上を実証しています。

Sitater

"Our approach shows significant improvements such as 33% for CLIP and 37% for LLaVA on our Flickr30k-Positions benchmark."
"Our method empirically demonstrates a significant performance boost by fine-tuning CLIP and LLaVA using our data generation pipeline on benchmarks such as SugarCrepe."

Viktige innsikter hentet fra

CounterCurate

by Jianrui Zhan... klokken arxiv.org 03-13-2024

https://arxiv.org/pdf/2402.13254.pdf

Dypere Spørsmål

他のデータセットでもCounterCurateの効果が同じように現れる可能性はありますか？

CounterCurateのアプローチは、物理的な根拠を持つ推論や意味論的なカウンターファクトを生成する能力を向上させることに焦点を当てています。このアプローチは、異なるデータセットでも同様に効果的である可能性があります。他のデータセットでも同様の問題や課題が存在し、CounterCurateがこれらの問題に対処する方法が適用できるかもしれません。