安裝中文字典英文字典辭典工具!
安裝中文字典英文字典辭典工具!
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- Sequence Length is a Domain: Length-based Overfitting in . . .
We demonstrate on a simple string editing task and a machine translation task that the Transformer model performance drops significantly when facing sequences of length diverging from the length distribution in the training data
- Assessing the Impact of Sequence Length Learning on . . .
To expose the datasets to the sequence length problem, we inject this spurious feature by creating a sequence length imbalance in the training data, and we partition the test set to assess the behavior of the model for different overlaps of distribution
- Transformer Encoder give worse performance when the sequence . . .
When you change the sequence length from 180 to 600, the model's learned dependencies on these encodings may not map correctly as the relationships between positions are distorted To resolve this issue you may try alternative positional embeddings such as rotary positional embedding (RoPE) or relative position encodings used in Transformer-XL
- Optimizing LLM Training with Variable Sequence Lengths . . .
When sequences exceed the max length and are split, how does this affect the model’s ability to learn dependencies like p (m+1|m)? What strategies or techniques are effective in ensuring that critical sequence transitions are properly trained and optimized for model performance?
- Optimizing Transformer Models for Variable-Length Input . . .
In this section, we demonstrated how the HuggingFace APIs allow us to leverage the optimized kernels in FlashAttention2, significantly boosting the training performance of existing models on sequences of varying length
- Tuning-Free Longer Context Lengths For LLMs - Bhavin Jawade
However, this mechanism is based on the maximum sequence length seen during training When the input sequence exceeds this length, the model encounters positions it has never seen before (O O D ), leading to a decrease in performance
- Why do Transformers have a sequence limit at inference time?
Transformer models have limited sequence length at inference time because of positional embeddings But there are workarounds Self-attention in transformer does not distinguish the order of keys values, it works as if the sequence is a bag of words
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