https://chatgpt.com/share/98a22c79-4119-4d07-ac17-9712301d7ae0
BERT and scGPT use different architectures (encoder-only and decoder-only, respectively) due to their specific pre-training objectives and intended applications. Here's an explanation of why each model uses its chosen architecture and how it relates to their pre-training:
BERT (Bidirectional Encoder Representations from Transformers):
1. Encoder-only architecture:
- BERT uses only the encoder part of the transformer architecture.
- This allows for bidirectional processing of input text, meaning it can look at both left and right context simultaneously.
2. Pre-training objective:
- Masked Language Modeling (MLM): BERT is trained to predict masked tokens in a sentence, given both left and right context.
- Next Sentence Prediction (NSP): It also predicts whether two sentences follow each other in the original text.
3. Relation to pre-training:
- The encoder-only architecture is well-suited for these tasks because they require understanding the full context of a sentence or pair of sentences.
- Bidirectional attention allows BERT to capture complex relationships between words in a sentence, which is crucial for predicting masked tokens accurately.
4. Intended use:
- BERT is designed for tasks that require deep understanding of text, such as classification, named entity recognition, and question answering.
- It's not meant for text generation tasks.
scGPT (Single-Cell Generative Pre-trained Transformer):
1. Decoder-only architecture:
- scGPT uses only the decoder part of the transformer architecture.
- This allows for autoregressive prediction, where each token is predicted based on the previous tokens.
2. Pre-training objective:
- Generative pre-training: scGPT is trained to predict gene expression values in a forced sequential manner.
- It uses a specialized attention masking technique to handle non-sequential single-cell data.
3. Relation to pre-training:
- The decoder-only architecture is well-suited for this task because it allows the model to generate expression values one at a time, considering the previously generated values.
- This approach enables the model to capture complex dependencies between genes and learn cell-type specific patterns.
4. Intended use:
- scGPT is designed for tasks related to single-cell multi-omics data, such as cell type annotation, multi-omics integration, and perturbation prediction.
- It can generate unknown gene expression values given known gene values or generate whole genome expression given a known cell type condition.
In summary, the choice of encoder-only for BERT and decoder-only for scGPT is directly related to their pre-training objectives and intended applications. BERT's bidirectional processing is ideal for understanding context in natural language, while scGPT's autoregressive approach is suitable for generating gene expression values in single-cell data. These architectural choices allow each model to excel in its specific domain and tasks.
Citations:
[1] https://vaclavkosar.com/ml/Encoder-only-Decoder-only-vs-Encoder-Decoder-Transfomer
[2] https://datascience.stackexchange.com/questions/65241/why-is-the-decoder-not-a-part-of-bert-architecture
[3] https://ai.stackexchange.com/questions/41505/which-situation-will-helpful-using-encoder-or-decoder-or-both-in-transformer-mod
[4] https://www.linkedin.com/pulse/transformer-architectures-dummies-part-2-decoder-only-bhaskar-t-hj9xc
[5] https://www.reddit.com/r/MLQuestions/comments/l1eiuo/when_would_we_use_a_transformer_encoder_only/
No, scGPT's masked training approach is not similar to BERT's. While both models use masking techniques, they differ significantly in their implementation and purpose:
1. BERT's Masked Language Model (MLM):
- BERT randomly masks tokens in the input sequence and predicts these masked tokens.
- It uses bidirectional context, looking at both left and right contexts to predict the masked tokens.
- This approach is designed for understanding the full context of text in natural language processing tasks.
2. scGPT's Specialized Attention Masking:
- scGPT uses a unique attention masking technique designed specifically for non-sequential single-cell data[1].
- It supports both gene-prompt and cell-prompt generations in a unified way.
- The masking defines the order of prediction based on attention scores, not on sequential order as in text.
- It allows attention computation only between embeddings of "known genes" and the query gene itself[1].
Key differences:
1. Purpose: BERT's masking is for understanding context in text, while scGPT's is for generating gene expression values in non-sequential data.
2. Directionality: BERT is bidirectional, while scGPT uses a form of forced sequential prediction in non-sequential data.
3. Prediction target: BERT predicts masked tokens, while scGPT predicts gene expression values.
4. Context use: BERT uses full context, while scGPT uses known genes to predict unknown genes iteratively.
5. Architecture: BERT is an encoder-only model, while scGPT is a decoder-only model, more similar to GPT architectures[1].
In summary, while both use masking, scGPT's approach is specifically tailored for single-cell genomic data and is fundamentally different from BERT's text-based masking approach.
Citations:
[1] https://www.biorxiv.org/content/10.1101/2023.04.30.538439v1.full
[2] https://www.reddit.com/r/deeplearning/comments/17gmtxr/bert_explained_training_masked_language_model/
[3] https://softteco.com/blog/bert-vs-chatgpt
[4] https://blog.invgate.com/gpt-3-vs-bert
[5] https://www.biorxiv.org/content/10.1101/2023.04.30.538439v2.full
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