1 citations · 1 across the 11 of their papers we have counts for
11 papers
CuraLight: Debate-Guided Data Curation for LLM-Centered Traffic Signal Control
Qing Guo, Xinhang Li, Junyu Chen +4
Traffic signal control (TSC) is a core component of intelligent transportation systems (ITS), aiming to reduce congestion, emissions, and travel time. Recent approaches based on re…
Utilizing Metadata for Better Retrieval-Augmented Generation
Raquib Bin Yousuf, Shengzhe Xu, Mandar Sharma +3
Retrieval-Augmented Generation systems depend on retrieving semantically relevant document chunks to support accurate, grounded outputs from large language models. In structured an…
Can an LLM Induce a Graph? Investigating Memory Drift and Context Length
Raquib Bin Yousuf, Aadyant Khatri, Shengzhe Xu +2
Recently proposed evaluation benchmarks aim to characterize the effective context length and the forgetting tendencies of large language models (LLMs). However, these benchmarks of…
When can isotropy help adapt LLMs' next word prediction to numerical domains?
Rashed Shelim, Shengzhe Xu, Walid Saad +1
Vector representations of contextual embeddings learned by pre-trained large language models (LLMs) are effective in various downstream tasks in numerical domains such as time seri…
The Prompt is Mightier than the Example
Shengzhe Xu, Nikhil Muralidhar, Naren Ramakrishnan
Numerous recent prompt optimization approaches like chain-of-thought, have been demonstrated to significantly improve the quality of content generated by large language models (LLM…
Optimizing Product Provenance Verification using Data Valuation Methods
Raquib Bin Yousuf, Hoang Anh Just, Shengzhe Xu +8
Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives…
LLM Augmentations to support Analytical Reasoning over Multiple Documents
Raquib Bin Yousuf, Nicholas Defelice, Mandar Sharma +2
Building on their demonstrated ability to perform a variety of tasks, we investigate the application of large language models (LLMs) to enhance in-depth analytical reasoning within…
Information Guided Regularization for Fine-tuning Language Models
Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu +2
The pretraining-fine-tuning paradigm has been the de facto strategy for transfer learning in modern language modeling. With the understanding that task adaptation in LMs is often a…
Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)
Shengzhe Xu, Cho-Ting Lee, Mandar Sharma +3
Synthetic data generation is integral to ML pipelines, e.g., to augment training data, replace sensitive information, and even to power advanced platforms like DeepSeek. While LLMs…
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
Shengzhe Xu, Christo Kurisummoottil Thomas, Omar Hashash +3
Large language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. However, recent efforts on LLMs for wireless networks are limited to…
STAN: Synthetic Network Traffic Generation with Generative Neural Models
Shengzhe Xu, Manish Marwah, Martin Arlitt +1
Deep learning models have achieved great success in recent years but progress in some domains like cybersecurity is stymied due to a paucity of realistic datasets. Organizations ar…