Natural Language Processing (NLP) and Natural Language Generation (NLG) have been revolutionized by the emergence of Large Language Models (LLMs) like GPT4V, Claude, and Gemini. These models combine visual encoders and LLMs, delivering exceptional performance with text-only or combined image and text inputs.
A benchmark dataset called IsoBench has been introduced, which includes challenges from games, science, mathematics, and algorithms. It allows thorough examination of performance disparities resulting from different input representations.
To address performance discrepancies in foundation models based on input representation, two strategies have been proposed: IsoCombination and IsoScratchPad. These aim to mitigate performance gaps and enhance model performance across diverse input modalities.
The research team has introduced IsoBench, an extensive test dataset spanning various topics and offering comprehensive multimodal performance evaluations. They have also evaluated well-known foundation models and suggested methods to bridge performance gaps between input modalities, resulting in improved model performance.
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Useful Links:
— AI Lab in Telegram @aiscrumbot – free consultation
— IsoBench: An Artificial Intelligence Benchmark Dataset Containing Problems from Four Major Areas: Math, Science, Algorithms, and Games
— MarkTechPost
— Twitter – @itinaicom
