Streaming generation reduces wait time by answering while you type
Answering While You Type: Optimizing Time-to-Answer with Streaming Generation
Computation and Language
Summary
Getting answers from language models usually means typing your full question and then waiting for a response. The authors show it’s possible for the model to start generating an answer before you finish typing, updating it as more of your question arrives. This approach can make the wait time shorter because the model does part of the work early. They tested this with different types of language models and studied when this method really helps speed up getting answers.
What this means in practice
- •For ai product developers: Enable chatbots to start generating replies before user finishes typing, reducing overall response wait time.$Commercial implications: Improves commercial conversational AI responsiveness, directly enhancing user experience in customer-facing products.
- •For interactive system engineers: Build language interfaces that update answers in real time as input is entered, shortening perceived response delays.
Authors
Nikita Dragunov, Matvey Mikhalchuk, Anton Razzhigaev
Abstract
Work on efficient large-language-model inference has concentrated almost entirely on reducing the cost of decoding once a request is complete. The quantity a user experiences, however, is the interval between submitting a request and receiving a usable response, and that interval can be shortened without making decoding itself faster. While a request is being composed it is already partially specified, yet conventional deployments begin no computation until submission. We propose and study streaming generation, a decoding regime in which the model begins producing a response from an incomplete request and revises it as the remaining input arrives, so that only a small residue of the answer remains to be computed once the request is submitted. We evaluate the regime on autoregressive and diffusion language models, characterise the conditions under which it reduces user-visible latency, and analyse the mechanism responsible.