7 citations · 10 across the 9 of their papers we have counts for
9 papers
BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute
Dujian Ding, Ankur Mallick, Shaokun Zhang +7
Large language models (LLMs) are powerful tools but are often expensive to deploy at scale. LLM query routing mitigates this by dynamically assigning queries to models of varying c…
Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs
Xiang Zhang, Juntai Cao, Jiaqi Wei +2
Despite the remarkable successes of large language models (LLMs), the underlying Transformer architecture has inherent limitations in handling complex reasoning tasks. Chain-of-tho…
ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries
Keke Huang, Yimin Shi, Dujian Ding +4
In recent years, large language models (LLMs) have demonstrated remarkable capabilities in comprehending and generating natural language content, attracting widespread attention in…
EcoAct: Economic Agent Determines When to Register What Action
Shaokun Zhang, Jieyu Zhang, Dujian Ding +7
Recent advancements have enabled Large Language Models (LLMs) to function as agents that can perform actions using external tools. This requires registering, i.e., integrating tool…
Supervised Chain of Thought
Xiang Zhang, Dujian Ding
Large Language Models (LLMs) have revolutionized natural language processing and hold immense potential for advancing Artificial Intelligence. However, the core architecture of mos…
OCCAM: Towards Cost-Efficient and Accuracy-Aware Classification Inference
Dujian Ding, Bicheng Xu, Laks V. S. Lakshmanan
Classification tasks play a fundamental role in various applications, spanning domains such as healthcare, natural language processing and computer vision. With the growing popular…
Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
Dujian Ding, Ankur Mallick, Chi Wang +5
Large language models (LLMs) excel in most NLP tasks but also require expensive cloud servers for deployment due to their size, while smaller models that can be deployed on lower c…
LLM Performance Predictors are good initializers for Architecture Search
Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan +1
In this work, we utilize Large Language Models (LLMs) for a novel use case: constructing Performance Predictors (PP) that estimate the performance of specific deep neural network a…
On Efficient Approximate Queries over Machine Learning Models
Dujian Ding, Sihem Amer-Yahia, Laks VS Lakshmanan
The question of answering queries over ML predictions has been gaining attention in the database community. This question is challenging because the cost of finding high quality an…