6 papers
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…
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…
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…
Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation
Shailik Sarkar, Raquib Bin Yousuf, Linhan Wang +9
Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products t…
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…
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…