Understanding Decoding Strategies in Large Language Models (LLMs)

Large Language Models (LLMs) are trained to predict the next word in a text sequence. However, the method by which they generate text involves a combination of their probability estimates and algorithms known as decoding strategies. These strategies are crucial in determining how LLMs choose the next word, according to AssemblyAI.

Next-Word Predictors vs. Text Generators

LLMs are often described as “next-word predictors” in non-scientific literature, but this can lead to misconceptions. During the decoding phase, LLMs employ various strategies to generate text, not just outputting the most probable next word iteratively. These strategies are known as decoding strategies, and they fundamentally determine how LLMs generate text.

Decoding Strategies

Decoding strategies can be divided into deterministic and stochastic methods. Deterministic methods produce the same output for the same input, while stochastic methods introduce randomness, leading to varied outputs even with the same input.

Deterministic Methods

Greedy Search

Greedy search is the simplest decoding strategy, where at each step, the most probable next token is chosen. While efficient, it often produces repetitive and dull text.

Beam Search

Beam search generalizes greedy search by maintaining a set of the top K most probable sequences at each step. While it improves text quality, it can still produce repetitive and unnatural text.

Stochastic Methods

Top-k Sampling

Top-k sampling introduces randomness by sampling the next token from the top k most probable choices. However, choosing an optimal k value can be challenging.

Top-p Sampling (Nucleus Sampling)

Top-p sampling dynamically selects tokens based on a cumulative probability threshold, adapting to the distribution shape at each step and preserving diversity in generated text.

Temperature Sampling

Temperature sampling adjusts the sharpness of the probability distribution using a temperature parameter. Lower temperatures produce more deterministic text, while higher temperatures increase randomness.

Optimizing Information-Content via Typical Sampling

Typical sampling introduces principles from information theory to balance predictability and surprise in generated text. It aims to produce text with average entropy, maintaining coherence and engagement.

Boosting Inference Speed via Speculative Sampling

Speculative sampling, recently discovered by Google Research and DeepMind, improves inference speed by generating multiple tokens per model pass. It involves a draft model generating tokens, followed by a target model verifying and correcting them, leading to significant speedups.

Conclusion

Understanding decoding strategies is crucial for optimizing the performance of LLMs in text generation tasks. While deterministic methods like greedy search and beam search provide efficiency, stochastic methods like top-k, top-p, and temperature sampling introduce necessary randomness for more natural outputs. Novel approaches like typical sampling and speculative sampling offer further improvements in text quality and inference speed, respectively.

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