Auditing a Candidate Completion Using Softmax Next-Token Probabilities and Autoregressive Log-Probability
You are reviewing an internal evaluation report for a customer-support LLM. The report claims the model would prefer Completion A over Completion B for the same prompt because “A has higher probability.” You suspect the analyst mixed up logits, probabilities, and sequence scoring.
Using ONLY the information below, determine which completion is actually more likely under the model (i.e., has higher conditional log-probability given the prompt), and briefly explain the reasoning steps you used (including how softmax, next-token conditional probabilities, and autoregressive decomposition combine into a single sequence score).
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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
Ch.1 Pre-training - Foundations of Large Language Models
Ch.5 Inference - Foundations of Large Language Models
Data Science
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An auto-regressive language model is generating text following the input 'The cat sat on the'. The model's objective is to find the output sequence with the highest total log-probability. It is considering two possible two-word continuations:
Path A: 'warm mat'
- log Pr('warm' | 'The cat sat on the') = -0.9
- log Pr('mat' | 'The cat sat on the warm') = -1.5
Path B: 'plush rug'
- log Pr('plush' | 'The cat sat on the') = -1.2
- log Pr('rug' | 'The cat sat on the plush') = -1.1
Based on the provided conditional log-probabilities, which path will the model choose and why?
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