Computer Science > Networking and Internet Architecture
[Submitted on 23 Jan 2024 (v1), last revised 16 Jun 2024 (this version, v2)]
Title:Eloquent: A More Robust Transmission Scheme for LLM Token Streaming
View PDF HTML (experimental)Abstract:To render each generated token in real-time for users, the Large Language Model (LLM) server generates tokens one by one and streams each token (or group of a few tokens) through the network to the user right after generation, which we refer to as LLM token streaming. However, under unstable network conditions, the LLM token streaming experience could suffer greatly from stalls since one packet loss could block the rendering of later tokens even if the packets containing them arrive on time. With a measurement study, we show that current applications suffer from increased stalls under unstable networks.
For this emerging token streaming problem in LLM Chatbots that differs from previous multimedia and text applications, we propose a novel transmission scheme, called Eloquent, which puts newly generated tokens as well as currently unacknowledged tokens in the next outgoing packet. This ensures that each packet contains some new tokens and, in the meantime, is independently rendered when received, avoiding the aforementioned stalls caused by missing packets. Through simulation under various networks, we show Eloquent reduces stall ratio (proportion of token rendering wait time) by 71.0% compared to the retransmission method commonly used by real chatbot applications and by 31.6% compared to the baseline packet duplication scheme. By tailoring Eloquent to fit the token-by-token generation of LLM, we enable the Chatbots to respond like an eloquent speaker for users to better enjoy pervasive AI.
Submission history
From: Hanchen Li [view email][v1] Tue, 23 Jan 2024 18:45:27 UTC (2,918 KB)
[v2] Sun, 16 Jun 2024 17:17:41 UTC (2,843 KB)
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