6 citations · 31 across the 20 of their papers we have counts for
22 papers
Credit Cards, Confusion, Computation, and Consequences: What Can We Uncover About Language Model Reasoning?
Arnav Hiray, Agam Shah, Caleb Lu +3
We introduce CreditCardQA, the first financial literacy benchmark for numerical reasoning derived from real credit card agreements. The dataset contains 1,800 questions, including…
Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement
Liqin Ye, Agam Shah, Chao Zhang +1
The traditional process of creating labeled datasets is labor-intensive and expensive. Recent breakthroughs in open-source large language models (LLMs) have opened up a new avenue…
VideoConviction: A Multimodal Benchmark for Human Conviction and Stock Market Recommendations
Michael Galarnyk, Veer Kejriwal, Agam Shah +4
Social media has amplified the reach of financial influencers known as "finfluencers," who share stock recommendations on platforms like YouTube. Understanding their influence requ…
Words That Unite The World: A Unified Framework for Deciphering Central Bank Communications Globally
Agam Shah, Siddhant Sukhani, Huzaifa Pardawala +24
Central banks around the world play a crucial role in maintaining economic stability. Deciphering policy implications in their communications is essential, especially as misinterpr…
KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation
Nikita Tatarinov, Vidhyakshaya Kannan, Haricharana Srinivasa +7
We introduce KG-MuLQA (Knowledge-Graph-based Multi-Level Question-Answer Extraction): a framework that (1) extracts QA pairs at multiple complexity levels (2) along three key dimen…
Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities
Nikita Tatarinov, Siddhant Sukhani, Agam Shah +1
Recent advances in language modeling have led to a growing number of papers related to finance in top-tier Natural Language Processing (NLP) venues. To systematically examine this…
Beyond the Reported Cutoff: Where Large Language Models Fall Short on Financial Knowledge
Agam Shah, Liqin Ye, Sebastian Jaskowski +2
Large Language Models (LLMs) are frequently utilized as sources of knowledge for question-answering. While it is known that LLMs may lack access to real-time data or newer data pro…
How Inclusively do LMs Perceive Social and Moral Norms?
Michael Galarnyk, Agam Shah, Dipanwita Guhathakurta +2
This paper discusses and contains offensive content. Language models (LMs) are used in decision-making systems and as interactive assistants. However, how well do these models maki…
CoCoHD: Congress Committee Hearing Dataset
Arnav Hiray, Yunsong Liu, Mingxiao Song +2
U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive d…
SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis
Huzaifa Pardawala, Siddhant Sukhani, Agam Shah +7
Fact-checking is extensively studied in the context of misinformation and disinformation, addressing objective inaccuracies. However, a softer form of misinformation involves respo…
BERTScoreVisualizer: A Web Tool for Understanding Simplified Text Evaluation with BERTScore
Sebastian Jaskowski, Sahasra Chava, Agam Shah
The BERTScore metric is commonly used to evaluate automatic text simplification systems. However, current implementations of the metric fail to provide complete visibility into all…
ConfReady: A RAG based Assistant and Dataset for Conference Checklist Responses
Michael Galarnyk, Rutwik Routu, Vidhyakshaya Kannan +4
The ARR Responsible NLP Research checklist website states that the "checklist is designed to encourage best practices for responsible research, addressing issues of research ethics…
Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis
Agam Shah, Arnav Hiray, Pratvi Shah +6
In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for public…
Abnormal Trading Detection in the NFT Market
Mingxiao Song, Yunsong Liu, Agam Shah +1
The Non-Fungible-Token (NFT) market has experienced explosive growth in recent years. According to DappRadar, the total transaction volume on OpenSea, the largest NFT marketplace,…
Shifting Cryptocurrency Influence: A High-Resolution Network Analysis of Market Leaders
Arnav Hiray, Pratvi Shah, Vishwa Shah +3
Over the last decade, the cryptocurrency market has experienced unprecedented growth, emerging as a prominent financial market. As this market rapidly evolves, it necessitates re-e…
Zero is Not Hero Yet: Benchmarking Zero-Shot Performance of LLMs for Financial Tasks
Agam Shah, Sudheer Chava
Recently large language models (LLMs) like ChatGPT have shown impressive performance on many natural language processing tasks with zero-shot. In this paper, we investigate the eff…
Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis
Agam Shah, Suvan Paturi, Sudheer Chava
Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a major driver of financial market returns. We construct the largest tokenized and annotated dataset of F…
The Universal NFT Vector Database: A Scaleable Vector Database for NFT Similarity Matching
Samrat Sahoo, Nitin Paul, Agam Shah +2
Non-Fungible Tokens (NFTs) are a type of digital asset that represents a proof of ownership over a particular digital item such as art, music, or real estate. Due to the non-fungib…
FiNER-ORD: Financial Named Entity Recognition Open Research Dataset
Agam Shah, Abhinav Gullapalli, Ruchit Vithani +2
Over the last two decades, the development of the CoNLL-2003 named entity recognition (NER) dataset has helped enhance the capabilities of deep learning and natural language proces…
Benchmarking Machine Learning Models to Predict Corporate Bankruptcy
Emmanuel Alanis, Sudheer Chava, Agam Shah
Using a comprehensive sample of 2,585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly t…
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…
Deep Reinforcement Learning for Conversational AI
Mahipal Jadeja, Neelanshi Varia, Agam Shah
Deep reinforcement learning is revolutionizing the artificial intelligence field. Currently, it serves as a good starting point for constructing intelligent autonomous systems whic…