Schematic of Probability Calculation in Causal Language Modeling

An autoregressive language model is given the sequence of tokens: 'The', 'cat', 'sat', 'on', 'the'. It is now tasked with predicting the very next token. Which of the following expressions correctly represents the primary calculation the model performs to determine the likelihood of the word 'mat' appearing next?

Contextual Influence on Token Probability

Analyzing Contextual Influence on Next-Token Probability

You’re reviewing an internal evaluation script tha...

Your team is building an internal tool that ranks ...

You’re reviewing an internal LLM evaluation pipeli...

Reconciling Training Log-Likelihood with Inference-Time Sequence Selection

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

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

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

Design a Correct Sequence-Scoring Function for Autoregressive LLM Outputs

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

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

Neural Network-Based Next-Token Probability Distribution

Initial Token Probability Assumption

Training Decoder-Only Language Models with Cross-Entropy Loss

Output Probability Calculation in Transformer Language Models

Global Nature of Standard Transformer LLMs

Processing Flow of Autoregressive Generation in a Decoder-Only Transformer

Initial Input Representation for Transformer Layers

Greedy Decoding in Language Models

Structure of a Transformer Block

A generative language model is designed to produce text by predicting the next token based solely on the sequence of tokens that came before it. If you were to adapt a standard Transformer decoder block for this specific auto-regressive task, which of its sub-layers would you remove, and why is this modification functionally necessary?

A language model is constructed using a stack of modified Transformer decoder blocks. Each block contains a self-attention sub-layer and a feed-forward network sub-layer, but lacks the sub-layer that would process information from a separate, secondary input sequence. This model is capable of performing a machine translation task, such as translating a German sentence into English, without any further architectural changes.

Function of Self-Attention in Auto-regressive Generation

Neural Network-Based Next-Token Probability Distribution