There have been many discussions regarding use of AI to study, understand and translate Early Buddhist Texts (EBTs).
But if you, like me, have been wondering how does a Generative Pretrained Transformer (GPT) model actually work, in simple enough terms, this video may help!
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Transformer models like GPT have driven recent advances in AI by powerfully modeling relationships between tokens in text/inputs through attention and vector representations. This allows generation of coherent text.
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Understanding the core components of Transformers like attention blocks, embeddings, and feedforward layers is important for interpreting and advancing how these large language models work and can be meaningfully used in different contexts, including study, translation and analysis of EBTs.
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While powerful, Transformers have limitations in modeling long-range context due to fixed context size, which can cause loss of coherence over many exchanges like in long conversations. Expanding context capabilities would help address this, but the inherent “mirage like” text creation logic remains.
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Transformers learn nuanced embedding representations of words/concepts based on their contexts, but these embeddings are still limited by the model’s pretrainig data and may reflect unintended biases. More diverse, balanced data could help mitigate biases and this can be very relevant in the context of translating EBTs with GPT.
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Openly sharing details on key aspects like attention mechanisms could help more researchers understand and advance these impactful models, but some details may remain behind paywalls for profit maximising reasons.
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Overall, the video provides helpful insight into foundational elements of powerful generative models like GPT, while also surfacing opportunities to expand their contexts, mitigate biases, enhance controllability, and increase transparency.
Hope it helps
