Everything LLMs

Papers🔗

• Attention operation - local context alignment
  • Cross-attention in the original decoder architecture had two blocks - first a self-attention block and then a cross-attention block where K,V come from input sequence, and Q from the output sequence.
  • Original transformer used dropout after each attention layer before the normalization, and also label smoothing for training.
  • Scaling by $\sqrt(d_k)$ since the variance of the dot product grows as $d_k$
• Positional encoding because the attention operation is a set operation
  • Sinusoidal embeddings use frequencies $2\pi$ to $20000\pi$,
  • Relative positional encoding adds a vector at each layer to the projected K/V embeddings $a_{ij} = w_{j-i}$, for a window of $2k + 1$, i.e. $k$ on both sides of the token. Beyond the window, we clip.
  • Rotary Positional Embedding is simply rotating the affine-transformed word embedding vector by amount of angle multiples of its position index, instead of adding to the vector
• GPT
  • Original GPT model used language modeling objective with a decoder-only model, with the embedding weights tied at the final linear layer.
  • A second stage was used for task-specific tuning, with both the language modeling objective and regularized by the actual prediction task. Delimiter tokens are also tokens.
  • GELU was used for activations and positional embeddings were learned.
• GPT-2
  • Use the Layer normalization at the inputs instead of outputs.
  • It shows good zero-shot performance, and no need for task-specific finetuning.
• T5 takes the approach of treating every problem as a text-to-text problem.
  • LayerNorm does not apply to bias term.
  • Position embeddings are simply a scalar addition to the corresponding logit used for attention, and shared across all layers, clipped until a logarithmic series up to 128.
  • Heavily curated Common Crawl dataset.
  • Span corruption is used as the key objective, where corrupted tokens are replaced by sentinel tokens in the input (multiple tokens also replaced with a single one), and the outputs are delimited by sentinel tokens.
  • Encoder-decoder models with the denoising objective generally tends to get the best performance out. But difference in details of the denoising objective don’t quite seem to matter much.

• UL2 proposes Mixture-of-Denoisers (MoD)
  • The conjecture is that distinguishing between different denoising tasks is beneficial for general learning. In addition, mode-switching via sentinel tokens for different downstream tasks is enabled.
  • The denoiser modes - Regular Denoiser (like T5), Sequential Denoiser (like causal LMs), and Extreme Denoiser
  • An additional paradigm token is used for mode-switching between the three denoisers.
  • Model is similar to T5.
• Encoder-only, Decoder-only, and Encoder-Decoder Models
  • There seems no reason to use encoder-only models alone. The cumbersomeness of task-specific classification heads on top of BERT-style encoder-only models makes them less appealing. Their generation capabilities are also limited.
  • Encoder-decoder models use separate parameters for inputs and targets, which interact only via cross-attention. Overall, the inductive biases of prefix LM decoder-only architectures may turn out to be similar to encoder-decoder architectures.
  • UL2 recommends that encoder-decoder architectures are generally preferable if storage is a concern. But in general, the self-supervision objective seems the more important choice.
• Lost in the Middle
  • Position of relevant information in the context significantly affects performance, with best when information is at the beginning or the end of the context.
  • Encoder-decoder models are relatively robust when information is within the training length.
  • Extended context models don’t necessarily improve retrieval performance in terms of using the input context.
  • The Attention Sinks observation seems in line with this paper.
• RoPE
  • RoPE proposes to incorporate the relative position information by multiplying with the sinusoidal functions, instead of adding.
  • We are looking for a function that only depends on only the relative position between source tokens and target tokens.
  • Due to rotations, does not change the norm, and the choice of the dimension-dependent rotation leads to decaying behavior.
• Llama
  • Use a mixture of diverse datasets, include C4 used in T5.
  • Applies normalization (RMSNorm) at the input of each layer instead of output.
  • Uses SwiGLU activation instead of ReLU.

Resources🔗

Common LLM Settings

Generating Text from Language Models (ACL 2023 Tutorial)

How to make LLMs go fast