35 citations · 61 across the 8 of their papers we have counts for
9 papers
KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn
Yoonjoo Lee, Hyoungwook Jin, Tae Soo Kim +3
To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content to a user's evolving underst…
Mandol: An Agglomerative Agent Memory System for Long-Term Conversations
Yuhan Zhang, Zhiyuan Guo, Ziheng Zeng +3
Long-term conversational agents need to remember and query cross-session, multi-typed information with complex correlations. Existing agent memory systems rely on heterogeneous vec…
Heterogeneous Computing: The Key to Powering the Future of AI Agent Inference
Yiren Zhao, Junyi Liu
AI agent inference is driving an inference heavy datacenter future and exposes bottlenecks beyond compute - especially memory capacity, memory bandwidth and high-speed interconnect…
UTune: Towards Uncertainty-Aware Online Index Tuning
Chenning Wu, Sifan Chen, Wentao Wu +4
There have been a flurry of recent proposals on learned benefit estimators for index tuning. Although these learned estimators show promising improvement over what-if query optimiz…
Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, Nicholas Pangakis +17
Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult…
EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Qingyan Guo, Rui Wang, Junliang Guo +6
Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper,…
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning
Yiding Jiang, Pierre Foret, Scott Yak +7
Understanding generalization in deep learning is arguably one of the most important questions in deep learning. Deep learning has been successfully adopted to a large number of pro…
Checking Observational Purity of Procedures
Himanshu Arora, Raghavan Komondoor, G. Ramalingam
Verifying whether a procedure is observationally pure is useful in many software engineering scenarios. An observationally pure procedure always returns the same value for the same…
On Hidden Variables: Value and Expectation No-Go Theorems
Andreas Blass, Yuri Gurevich
No-go theorems assert that hidden-variable theories, subject to appropriate hypotheses, cannot reproduce the predictions of quantum theory. We examine two species of such theorems,…