13 citations · 40 across the 13 of their papers we have counts for
13 papers
Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction
Harit Vishwakarma, Alan Mishler, Thomas Cook +3
Large language models (LLMs) are empowering decision-making in several applications, including tool or API usage and answering multiple-choice questions (MCQs). However, incorrect…
Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation
Ayan Sengupta, Vaibhav Seth, Arinjay Pathak +5
Large Language Models (LLMs) are highly resource-intensive to fine-tune due to their enormous size. While low-rank adaptation is a prominent parameter-efficient fine-tuning approac…
Global Graph Counterfactual Explanation: A Subgraph Mapping Approach
Yinhan He, Wendy Zheng, Yaochen Zhu +5
Graph Neural Networks (GNNs) have been widely deployed in various real-world applications. However, most GNNs are black-box models that lack explanations. One strategy to explain G…
Scalable Representation Learning for Multimodal Tabular Transactions
Natraj Raman, Sumitra Ganesh, Manuela Veloso
Large language models (LLMs) are primarily designed to understand unstructured text. When directly applied to structured formats such as tabular data, they may struggle to discern…
Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports
Tianyu Cao, Natraj Raman, Danial Dervovic +1
As large language models (LLMs) expand the power of natural language processing to handle long inputs, rigorous and systematic analyses are necessary to understand their abilities…
Synthetic Data Applications in Finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta +17
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applicatio…
DocLLM: A layout-aware generative language model for multimodal document understanding
Dongsheng Wang, Natraj Raman, Mathieu Sibue +6
Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalit…
Synthetic Text Generation using Hypergraph Representations
Natraj Raman, Sameena Shah
Generating synthetic variants of a document is often posed as text-to-text transformation. We propose an alternate LLM based method that first decomposes a document into semantic f…
Bayesian Hierarchical Models for Counterfactual Estimation
Natraj Raman, Daniele Magazzeni, Sameena Shah
Counterfactual explanations utilize feature perturbations to analyze the outcome of an original decision and recommend an actionable recourse. We argue that it is beneficial to pro…
WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
Raj Sanjay Shah, Kunal Chawla, Dheeraj Eidnani +7
Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training s…
Structure and Semantics Preserving Document Representations
Natraj Raman, Sameena Shah, Manuela Veloso
Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text. The inclusion of structural relationship betw…
Synthetic Document Generator for Annotation-free Layout Recognition
Natraj Raman, Sameena Shah, Manuela Veloso
Analyzing the layout of a document to identify headers, sections, tables, figures etc. is critical to understanding its content. Deep learning based approaches for detecting the la…
Robust Document Representations using Latent Topics and Metadata
Natraj Raman, Armineh Nourbakhsh, Sameena Shah +1
Task specific fine-tuning of a pre-trained neural language model using a custom softmax output layer is the de facto approach of late when dealing with document classification prob…