3 citations · 4 across the 8 of their papers we have counts for
9 papers
C3PO: Optimized Large Language Model Cascades with Probabilistic Cost Constraints for Reasoning
Antonios Valkanas, Soumyasundar Pal, Pavel Rumiantsev +2
Large language models (LLMs) have achieved impressive results on complex reasoning tasks, but their high inference cost remains a major barrier to real-world deployment. A promisin…
FEval-TTC: Fair Evaluation Protocol for Test-Time Compute
Pavel Rumiantsev, Soumyasundar Pal, Yingxue Zhang +1
The performance of Large Language Models (LLMs) and the associated dollar costs of API calls can fluctuate over time, potentially invalidating conclusions drawn in prior research.…
Half Search Space is All You Need
Pavel Rumiantsev, Mark Coates
Neural Architecture Search (NAS) is a powerful tool for automating architecture design. One-Shot NAS techniques, such as DARTS, have gained substantial popularity due to their comb…
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation
Pavel Rumiantsev, Mark Coates
Neural Architecture Search (NAS) is a powerful automatic alternative to manual design of a neural network. In the zero-shot version, a fast ranking function is used to compare arch…
Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
Ge Zhang, Mohammad Ali Alomrani, Hongjian Gu +7
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational reasoning problems such as kinship or spati…
Enhancing CTR Prediction in Recommendation Domain with Search Query Representation
Yuening Wang, Man Chen, Yaochen Hu +5
Many platforms, such as e-commerce websites, offer both search and recommendation services simultaneously to better meet users' diverse needs. Recommendation services suggest items…
Sparse Decomposition of Graph Neural Networks
Yaochen Hu, Mai Zeng, Ge Zhang +4
Graph Neural Networks (GNN) exhibit superior performance in graph representation learning, but their inference cost can be high, due to an aggregation operation that can require a…
Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
Jiaming Zhou, Abbas Ghaddar, Ge Zhang +7
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that in…
Graph Knowledge Distillation to Mixture of Experts
Pavel Rumiantsev, Mark Coates
In terms of accuracy, Graph Neural Networks (GNNs) are the best architectural choice for the node classification task. Their drawback in real-world deployment is the latency that e…