English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The great success of Large Language Models (LLMs) has expanded the potentialof multimodality, contributing to the gradual evolution of General ArtificialIntelligence (AGI). A true AGI agent should not only possess the capability toperform predefined multi-tasks but also exhibit emergent abilities in anopen-world context. However, despite the considerable advancements made byrecent multimodal LLMs, they still fall short in effectively unifyingcomprehension and generation tasks, let alone open-world emergent abilities. Wecontend that the key to overcoming the present impasse lies in enabling textand images to be represented and processed interchangeably within a unifiedautoregressive Transformer. To this end, we introduce SEED, an elaborate imagetokenizer that empowers LLMs with the ability to SEE and Draw at the same time.We identify two crucial design principles: (1) Image tokens should beindependent of 2D physical patch positions and instead be produced with a 1Dcausal dependency, exhibiting intrinsic interdependence that aligns with theleft-to-right autoregressive prediction mechanism in LLMs. (2) Image tokensshould capture high-level semantics consistent with the degree of semanticabstraction in words, and be optimized for both discriminativeness andreconstruction during the tokenizer training phase. With SEED tokens, LLM isable to perform scalable multimodal autoregression under its original trainingrecipe, i.e., next-word prediction. SEED-LLaMA is therefore produced bylarge-scale pretraining and instruction tuning on the interleaved textual andvisual data, demonstrating impressive performance on a broad range ofmultimodal comprehension and generation tasks. More importantly, SEED-LLaMA hasexhibited compositional emergent abilities such as multi-turn in-contextmultimodal generation, acting like your AI assistant.

Making LLaMA SEE and Draw with SEED Tokenizer