Adaptively Robust LLM Inference Optimization under Prediction Uncertainty

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901

Earlier work this paper cites.

A review of machine scheduling: Complexity, algorithms and approximability

Bo Chen, Chris N Potts, and Gerhard J Woeginger. 1998 · 1998

Earlier work this paper cites.

Resource-constrained project scheduling: Notation, classification, models, and methods

Peter Brucker, Andreas Drexl, Rolf Möhring, Klaus Neumann, and Erwin Pesch. 1999 · 1999

Earlier work this paper cites.

Parallel machine scheduling with splitting jobs

Wenxun Xing and Jiawei Zhang. 2000 · 2000

Earlier work this paper cites.

An approximation algorithm for scheduling two parallel machines with capacity constraints

Heng Yang, Yinyu Ye, and Jiawei Zhang. 2003 · 2003

Earlier work this paper cites.

The price of robustness

Dimitris Bertsimas and Melvyn Sim. 2004 · 2004

Earlier work this paper cites.

Handbook on scheduling: from theory to applications

Jacek Blazewicz, Klaus H Ecker, Erwin Pesch, Gunter Schmidt, and Jan Weglarz. 2007 · 2007

Earlier work this paper cites.

Logistics scheduling with batching and transportation

Bo Chen and Chung-Yee Lee. 2008 · 2008

Earlier work this paper cites.

Competitive two-agent scheduling and its applications

Joseph Y-T Leung, Michael Pinedo, and Guohua Wan. 2010 · 2010

Earlier work this paper cites.

A unified framework for dynamic prediction market design

Shipra Agrawal, Erick Delage, Mark Peters, Zizhuo Wang, and Yinyu Ye. 2011 · 2011

Earlier work this paper cites.

Parallel machine selection and job scheduling to minimize machine cost and job tardiness

Dong Cao, Mingyuan Chen, and Guohua Wan. 2005 · 2012

Earlier work this paper cites.

Metaheuristics for solving a multimodal home-healthcare scheduling problem

Gerhard Hiermann, Matthias Prandtstetter, Andrea Rendl, Jakob Puchinger, and Günther R Raidl. 2015 · 2015

Earlier work this paper cites.

Appointment scheduling with limited distributional information

Ho-Yin Mak, Ying Rong, and Jiawei Zhang. 2015 · 2015

Earlier work this paper cites.

Secretary and online matching problems with machine learned advice

Antonios Antoniadis, Themis Gouleakis, Pieter Kleer, and Pavel Kolev. 2020 · 2020

Earlier work this paper cites.

Scaling laws for neural language models

Original

Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2020

Earlier work this paper cites.

Character AI

Character. 2021 · 2021

Earlier work this paper cites.

GitHub Copilot

GitHub. 2021 · 2021

Cited alongside, same era.

Competitive caching with machine learned advice

Thodoris Lykouris and Sergei Vassilvitskii. 2021 · 2021

Cited alongside, same era.

Online bipartite matching with advice: Tight robustness-consistency tradeoffs for the two-stage model

Billy Jin and Will Ma. 2022 · 2022

Cited alongside, same era.

Perplexity AI

Perplexity. 2022 · 2022

Cited alongside, same era.

Emergent abilities of large language models

Original

Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022

Cited alongside, same era.

Orca: A distributed serving system for { \{ Transformer-Based } \} generative models. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22) . 521–538

Robust scheduling with gflownets

Original

David W Zhang, Corrado Rainone, Markus Peschl, and Roberto Bondesan. 2023 · 2023

Later among the works it cites.

LMSYS-Chat-1M: A large-scale real-world LLM conversation dataset

Original

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric Xing, et al · 2023

Later among the works it cites.

Response length perception and sequence scheduling: An llm-empowered llm inference pipeline

Zangwei Zheng, Xiaozhe Ren, Fuzhao Xue, Yang Luo, Xin Jiang, and Yang You. 2023b · 2023

Later among the works it cites.

Vidur: A Large-Scale Simulation Framework For LLM Inference

Amey Agrawal, Nitin Kedia, Jayashree Mohan, Ashish Panwar, Nipun Kwatra, Bhargav Gulavani, Ramachandran Ramjee, and Alexey Tumanov. 2024a · 2024

Later among the works it cites.

Taming throughput-latency tradeoff in LLM inference with Sarathi-Serve