A lightweight trained verifier sequentially accepts or rejects draft tokens, reducing calls to the target LLM and speeding up speculative decoding with minimal quality loss.
Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Controlled text generation (CTG) seeks to guide large language model (LLM) output to produce text that conforms to desired criteria. The current study presents a novel CTG algorithm that enforces adherence toward specific rhetorical relations in an LLM sentence-completion context by a parser-driven decoding scheme that requires no model fine-tuning. The method is validated both with automatic and human evaluation. The code is accessible on GitHub.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Speeding up Speculative Decoding via Sequential Approximate Verification
A lightweight trained verifier sequentially accepts or rejects draft tokens, reducing calls to the target LLM and speeding up speculative decoding with minimal quality loss.