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From the 1 of 10 linked papers with an AI index.

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10 papers

cs.CL2026

Credit Cards, Confusion, Computation, and Consequences: What Can We Uncover About Language Model Reasoning?

Arnav Hiray, Agam Shah, Caleb Lu +3

The paper presents CreditCardQA, a benchmark of 1,800 real‑world credit‑card agreement questions for testing numerical reasoning in language models, and shows that Program‑of‑Thoug…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…