Using Temperature with Softmax to Control Randomness in Token Selection
The randomness of token selection in large language models can be finely controlled by applying a temperature parameter, , to the Softmax function, which adjusts the sharpness of the probability distribution derived from the raw logits. A higher temperature value diminishes the differences between logits, making the probability distribution more uniform and giving all candidate tokens a more equal chance of being selected, thereby increasing the diversity of the generated output. Conversely, setting the temperature to a lower value sharpens the distribution, increasing the likelihood of selecting high-probability tokens and leading to more deterministic outputs. For instance, setting the Top- threshold to and the temperature close to zero makes the sampling process equivalent to a greedy search.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Next Token Prediction Task
Token Sampling from a Conditional Probability Distribution
Using Temperature with Softmax to Control Randomness in Token Selection
A language model is generating text and has produced the sequence 'The sky is'. It then calculates the following probability distribution for the next potential token:
{'blue': 0.75, 'green': 0.15, 'bright': 0.08, 'falling': 0.02}. If the model is configured to always select the single token with the highest probability, which token will it choose next?Analyzing Token Selection Strategies
A language model is generating text and encounters the same input sequence on two separate occasions, producing two different probability distributions for the next token, shown below.
- Distribution A:
{'meal': 0.90, 'dish': 0.05, 'surprise': 0.03, 'error': 0.02} - Distribution B:
{'soup': 0.30, 'stew': 0.25, 'salad': 0.22, 'dessert': 0.23}
Which of the following statements provides the most accurate analysis of these two distributions regarding the token selection process?
- Distribution A:
To ensure the generated text is as coherent and factually accurate as possible, a language model must always select the single token with the highest probability from the distribution at each step of the generation process.
Ranking and Top-p (Nucleus) Sampling Process
Comparison of Top-p and Top-k Sampling
A language model is generating text and has calculated the following probabilities for the next potential token:
{'the': 0.40, 'a': 0.30, 'one': 0.15, 'an': 0.10, 'some': 0.05}. If the model uses a sampling method where it selects from the smallest set of the most likely tokens whose cumulative probability exceeds a threshold ofp = 0.75, which set of tokens will it sample from?Effect of Parameter 'p' on Text Generation
Dynamic Candidate Set in Probabilistic Text Generation
You are tuning decoding for an internal "meeting-n...
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You’re building an internal “RFP response drafter”...
You’re implementing an LLM feature that generates ...
Post-incident analysis: fixing repetition and truncation by tuning decoding
Debugging Decoding: Balancing Determinism, Diversity, and Length in a Regulated Product
Selecting and Justifying a Decoding Policy for Two Production Use Cases
Choosing a Decoding Configuration Under Latency, Diversity, and Length Constraints
Release-readiness decision: decoding configuration for a customer-facing summarization feature
Decoding policy decision for a multilingual support assistant under safety, latency, and verbosity constraints
Balancing Randomness and Coherence in Token Sampling
Using Temperature with Softmax to Control Randomness in Token Selection
Learn After
Token Sampling from a Conditional Probability Distribution
Temperature-Scaled Softmax for Renormalized Probability
A language model has calculated the following raw scores (logits) for the next potential token:
{'mat': 3.0, 'rug': 2.5, 'chair': 2.0, 'moon': -1.0}. To control the randomness of the output, a temperature parameter is applied to these scores before they are converted into a final probability distribution for sampling. Which of the following probability distributions most likely resulted from applying a low temperature (e.g., a value less than 1.0)?Troubleshooting a Factual Chatbot's Output
You are configuring a text generation model for different tasks. Match each task with the description of the temperature setting that would be most appropriate to achieve the desired output.