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AI Interview Series #1: Explain Some LLM Text Generation Strategies Used in LLMs
The target audience for this article consists of business professionals, AI practitioners, and decision-makers interested in understanding the underlying mechanisms of large language models (LLMs) and their text generation strategies. Their primary pain points include the need for clarity on how AI can be effectively integrated into business processes, the desire for reliable and coherent outputs from AI systems, and the challenge of selecting the right strategies for specific applications. These individuals are goal-oriented, seeking to leverage AI for improved efficiency, creativity, and decision-making in their organizations. They are likely to appreciate clear, concise communication that balances technical detail with practical application.
Understanding Text Generation Strategies
Every time you prompt an LLM, it doesn’t generate a complete answer all at once — it builds the response one word (or token) at a time. At each step, the model predicts the probability of what the next token could be based on everything written so far. However, knowing probabilities alone isn’t enough — the model also needs a strategy to decide which token to pick next.
Different strategies can significantly change the final output, with some making it more focused and precise, while others enhance creativity or variation. In this article, we’ll explore four popular text generation strategies used in LLMs: Greedy Search, Beam Search, Nucleus Sampling, and Temperature Sampling.
Greedy Search
Greedy Search is the simplest decoding strategy where, at each step, the model picks the token with the highest probability given the current context. While it’s fast and easy to implement, it doesn’t always produce the most coherent or meaningful sequence. This approach can lead to repetitive, generic, or dull text, making it unsuitable for open-ended text generation tasks.
Beam Search
Beam Search improves upon greedy search by keeping track of multiple possible sequences (called beams) at each generation step instead of just one. It expands the top K most probable sequences, allowing the model to explore several promising paths in the probability tree. The parameter K (beam width) controls the trade-off between quality and computation — larger beams produce better text but are slower.
While beam search works well in structured tasks like machine translation, it tends to produce repetitive and predictable text in open-ended generation due to its preference for high-probability continuations.
Nucleus Sampling (Top-p Sampling)
Nucleus Sampling dynamically adjusts how many tokens are considered for generation at each step. Instead of picking from a fixed number of top tokens, it selects the smallest set of tokens whose cumulative probability adds up to a chosen threshold p (for example, 0.7). This allows the model to balance diversity and coherence, producing more natural and varied text compared to fixed-size methods.
Temperature Sampling
Temperature Sampling controls the level of randomness in text generation by adjusting the temperature parameter (t) in the softmax function that converts logits into probabilities. A lower temperature (t < 1) sharpens the distribution, increasing the chance of selecting the most probable tokens, resulting in more focused but often repetitive text. Higher temperatures (t > 1) flatten the distribution, introducing more randomness and diversity at the cost of coherence.
The optimal temperature often depends on the task — for instance, creative writing benefits from higher values, while technical or factual responses perform better with lower ones.
Understanding these strategies can help businesses effectively utilize LLMs for various applications, ensuring that the generated content meets their specific needs.
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