Viewing enriched Twitter post
How can we unlock generalized reasoning? ⚡️Introducing Energy-Based Transformers (EBTs), an approach that out-scales (feed-forward) transformers and unlocks generalized reasoning/thinking on any modality/problem without rewards. TLDR: - EBTs are the first model to outscale the… https://t.co/CHUVzp34hF
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"full_text": "@AlexiGlad Can we treat questions with high ambiguity as those that require \"high energy\" to answer? Prior work showed that high entropy logits do not correlate well with actual uncertainty in the question (the model is usually over-confident even after calibration). How is this different?",
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"text": "Yes, we can treat questions with high ambiguity as those with high energy!\n\nThe biggest difference in EBTs and feed-forward models is that the scalar energy is defined over the entire vocabulary distribution, and this distribution has passed through several layers. In other words, the model has had time to formulate representations of predictions throughout these layers, which I think is key. \n\nIntuitively, I think feed-forward models, making predictions in the output space, have much more of a challenge in formulating representations of predictions regarding uncertainty.",
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"full_text": "@derekchen14 Yes, we can treat questions with high ambiguity as those with high energy!\n\nThe biggest difference in EBTs and feed-forward models is that the scalar energy is defined over the entire vocabulary distribution, and this distribution has passed through several layers. In other…",
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"full_text": "@KnightAITech Thanks for the kind comments! I think in the short-term the biggest application will be data-bound domains, as EBTs are more data-efficient than the standard Transformer++. \n\nIn the long-term, I'd say the applications should be pretty general in anything requiring more reasoning!",
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"full_text": "@vanraj_narula There's a thread on that here for reference:\nhttps://t.co/Fuu2JuSLB1\nHope that helps!",
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"full_text": "@athundt Hi, apologies, I think there may be a misunderstanding! The claim wasn't that you can solve any problem, but rather that you can reason on any problem. Of course, problems that are simply uncomputable cannot be computed regardless of the model (such as the halting problem).",
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