Conditional Probability in Sequence-to-Sequence Generation

Next-Token Probability Calculation in Autoregressive Decoders

Example of Autoregressive Generation and Log-Probability Calculation

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?

Debugging a Generation Model's Choice

Greedy Decoding vs. Optimal Sequence Probability

Reconciling Training Log-Likelihood with Inference-Time Sequence Selection

Diagnosing a “High-Confidence Wrong Token” Bug in Autoregressive Scoring

Explaining a Counterintuitive Decoding Outcome Using Softmax, Next-Token Conditionals, and Sequence Log-Probability

Auditing a Candidate Completion Using Softmax Next-Token Probabilities and Autoregressive Log-Probability

Investigating a Production Scoring Bug: Softmax Normalization vs. Autoregressive Sequence Log-Probability

Root-Cause Analysis: Why a “More Likely” Token-by-Token Completion Loses on Total Sequence Score

Design a Correct Sequence-Scoring Function for Autoregressive LLM Outputs

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Direct Computation of Output Sequence Log-Probability in LLMs