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Meta Platforms confirmed off a bevy of recent AI options for its consumer-facing companies Fb, Instagram and WhatsApp at its annual Meta Join convention in Menlo Park, California, this week.
However the greatest information from Mark Zuckerberg’s firm might have truly come within the type of a pc science paper revealed with out fanfare by Meta researchers on the open entry and non-peer reviewed web site arXiv.org.
The paper introduces Llama 2 Lengthy, a brand new AI mannequin based mostly on Meta’s open supply Llama 2 launched in the summertime, however that has undergone “continuous pretraining from Llama 2 with longer coaching sequences and on a dataset the place lengthy texts are upsampled,” in accordance with the researcher-authors of the paper.
On account of this, Meta’s newly elongated AI mannequin outperforms a number of the main competitors in producing responses to lengthy (larger character rely) consumer prompts, together with OpenAI’s GPT-3.5 Turbo with 16,000-character context window, in addition to Claude 2 with its 100,000-character context window.
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How LLama 2 Lengthy got here to be
Meta researchers took the unique Llama 2 obtainable in its completely different coaching parameter sizes — the values of information and data the algorithm can change by itself because it learns, which within the case of Llama 2 are available 7 billion, 13 billion, 34 billion, and 70 billion variants — and included extra longer textual content knowledge sources than the unique Llama 2 coaching dataset. One other 400 billion tokens-worth, to be actual.
Then, the researchers stored the unique Llama 2’s structure the identical, and solely made a “vital modification to the positional encoding that’s essential for the mannequin to attend longer.”
That modification was to the Rotary Positional Embedding (RoPE) encoding, a technique of programming the transformer mannequin underlying LLMs akin to Llama 2 (and LLama 2 Lengthy), which primarily maps their token embeddings (the numbers used to signify phrases, ideas, and concepts) onto a 3D graph that reveals their positions relative to different tokens, even when rotated. This permits a mannequin to provide correct and useful responses, with much less data (and thus, much less computing storage taken up) than different approaches.
The Meta researchers “decreased the rotation angle” of its RoPE encoding from Llama 2 to Llama 2 Lengthy, which enabled them to make sure extra “distant tokens,” these occurring extra hardly ever or with fewer different relationships to different items of data, have been nonetheless included within the mannequin’s data base.
Utilizing reinforcement studying from human suggestions (RLHF), a typical AI mannequin coaching methodology the place AI is rewarded for proper solutions with human oversight to verify it, and artificial knowledge generated by Llama 2 chat itself, the researchers have been capable of enhance its efficiency in widespread LLM duties together with coding, math, language understanding, widespread sense reasoning, and answering a human consumer’s prompted questions.
With such spectacular outcomes relative to each Llama 2 common and Anthropic’s Claude 2 and OpenAI’s GPT-3.5 Turbo, it’s little marvel the open-source AI group on Reddit and Twitter and Hacker Information have been expressing their admiration and pleasure about Llama 2 because the paper’s launch earlier this week — it’s a giant validation of Meta’s “open supply” strategy towards generative AI, and signifies that open supply can compete with the closed supply, “pay to play” fashions supplied by well-funded startups.
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