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A team of researchers from Carnegie Mellon University introduced PANGEA, a multilingual multimodal LLM designed to bridge linguistic and cultural gaps in visual understanding tasks. PANGEA represents a significant step forward in creating inclusive and robust multilingual multimodal LLMs. Don’t Forget to join our 50k+ ML SubReddit.
However, acquiring such datasets presents significant challenges, including datascarcity, privacy concerns, and high data collection and annotation costs. Artificial (synthetic) data has emerged as a promising solution to these challenges, offering a way to generate data that mimics real-world patterns and characteristics.
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LLMs are suggested to complete the SiST task because of their enormous success with machine and spoken translation. Starting with the read-write policy, which requires LLM only to offer partial translation for input speech, integrating LLM into the SiST takes work. If you like our work, you will love our newsletter.
While deep learning’s scaling effects have driven advancements in AI, particularly in LLMs like GPT, further scaling during training faces limitations due to datascarcity and computational constraints. Also,dont forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup.
This surprising trend highlights the continued relevance of SLMs and raises important questions about their role in the LLM era, a topic previously overlooked in research. This study examines the role of SMs in the LLM era from two perspectives: collaboration with LLMs and competition against them.
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