Mathematical Justification for Greedy Search

Candidate Set in Greedy Search

A language model is generating a two-token sequence. At the first step, it calculates the probability for the next token: 'Token A' has a probability of 0.6, and 'Token B' has a probability of 0.4. If the model chooses 'Token A', the most probable subsequent token is 'Token C' (with a conditional probability of 0.5). If the model had chosen 'Token B', the most probable subsequent token would be 'Token D' (with a conditional probability of 0.9). A text generation algorithm is used that, at every

Algorithm Suitability for Text Generation Tasks

Analyzing Suboptimal Outcomes in Text Generation

Selecting and Justifying a Decoding Policy for Two Production Use Cases

Debugging Decoding: Balancing Determinism, Diversity, and Length in a Regulated Product

Post-incident analysis: fixing repetition and truncation by tuning decoding

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

You are tuning decoding for an internal "meeting-n...

You’re implementing an LLM feature that generates ...

You’re building an internal “RFP response drafter”...

You’re deploying an LLM to draft customer-facing i...

Greedy Search Strategy in Sequence-to-Sequence Models