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Comparison of Learned vs. Heuristic-Based Relative Positional Biases
Relative positional biases, which are added to the query-key product, can be implemented in two primary ways: they can be learned as parameters during training on a specific dataset, or they can be assigned fixed values based on pre-defined heuristics. The main trade-off is between the data-driven adaptability of learned biases and the training-free, direct applicability of heuristic-based biases.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Heuristic-Based Relative Positional Biases
Comparison of Learned vs. Heuristic-Based Relative Positional Biases
Kerple
FIRE
Relative Position Offset Calculation
A self-attention model incorporates positional awareness by adding a bias term directly to the query-key dot product for each pair of positions
(i, j). This bias term's value depends on the relative distance betweeniandj. What is the primary implication of this approach compared to the alternative of adding positional vectors to the input token embeddings?Incorporating Positional Bias into Attention Scores
In a self-attention mechanism, the score computed between a query at position
iand a key at positionjis modified by directly adding a bias term whose value depends only on the positionsiandj. What is the primary function of this bias term within the attention calculation?
Learn After
Choosing a Positional Bias Strategy for a Low-Resource Task
Selecting a Positional Bias Strategy for a Low-Data Scenario
A research team is developing a language model for a highly specialized domain with a very large, domain-specific training dataset. They hypothesize that the relationships between words in this domain follow unique, non-linear patterns that are not captured by simple distance metrics. Which implementation of relative positional biases would be most suitable for this project, and what is the primary reason?