scikit-learn

[Paper Club] BERT: Bidirectional Encoder Representations from Transformers

Nov 27, 2024 · 53:12

Eric Ness walks through the BERT paper, explaining how its bidirectional encoder architecture with masked language modeling and next sentence prediction pre-training enabled state-of-the-art results on 11 NLP tasks in 2019. He details the 110M parameter BASE and 340M parameter LARGE models, trained on 2.5B Wikipedia words and 800M book words, and how a simple logistic regression on BERT embeddings achieves 82% accuracy on IMDb sentiment classification, far above the 50% baseline. Swyx and Eric discuss how BERT’s pre-training objectives—masking 15% of tokens and predicting sentence order—teach small models word relationships for efficient classification, though they lose data compared to decoder-only next-token prediction. They note that while scaling laws favor decoder models, BERT-style encoders remain cost-effective for edge deployment, with recent work showing full retraining in 24 hours for under $500 or even 1 hour for $20 on 8 A100s, matching original GLUE scores.

LLM Asia Paper Club Survey Round

May 22, 2024 · 55:25

The episode surveys four recent papers on LLM reasoning, uncertainty, interpretability, and efficiency. 'Let's Think Dot by Dot' shows that filler tokens (dots) inserted between input and output enable hidden computation, outperforming no-token baselines on tasks like 3-Sum and 2-Sum Transform. 'Uncertainty Estimation' trains a random forest on hidden-layer activations to predict response confidence, achieving higher AUC than unsupervised methods on Q&A and translation. 'Monosemanticity' uses sparse autoencoders to identify interpretable features (e.g., a DNA-detection feature) in a toy transformer, advancing mechanistic interpretability. 'Medusa' attaches multiple prediction heads to the final hidden state to speculate future tokens, enabling faster decoding without a separate draft model and training in five hours on 60K samples.