Altman Declares Transformer's Death: AGI to Arrive in Two Years, Next-Gen Architecture on the Horizon - 36 Kr
Altman Declares Transformer's Death: AGI to Arrive in Two Years, Next-Gen Architecture on the Horizon 36 Kr
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Fine-tuning Whisper-large-v3 for child reading assessment with numerals and proper names
Hi everyone, I’m working on a reading assessment product for children. Current setup: a child reads a known passage for about 1 minute our system then counts how many words were read correctly right now we use whisper-1 as a baseline we now want to move to an open model and fine-tune Whisper-large-v3 on our own infrastructure This is not a generic ASR task: we always know the reference text in advance our main metric is correct-word-count accuracy against the reference passage The main cases we want to improve through fine-tuning are: numerals / spoken-written forms, for example “three” vs “3” proper names and other rare words child reading speech in general I’d like advice specifically on the fine-tuning strategy for this type of task. My questions: For this use case, what training target
Grokking Beyond Addition
Hi everyone, I’m excited to share my research paper: “Grokking Beyond Addition: Circuit-Level Analysis of Algebraic Learning in Transformers” Paper: https://zenodo.org/records/19256207 This work explores grokking across multiple algebraic structures and shows a clear result: At small model scale (d_model = 64), transformers reliably grok abelian tasks but fail to generalize on non-abelian groups , even with 100% training accuracy. It also highlights: Early circuit formation before generalization Evidence for discrete-log structure in multiplication Strong embedding similarity across different tasks (CKA) I’m opening this project for collaboration and contributions: Scaling experiments (d_model = 128 / 256) Extending to more algebraic structures Interpretability improvements Reproduction an
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be careful on what could run on your gpus fellow cuda llmers
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