6 citations · 26 across the 27 of their papers we have counts for
11 papers · 1 filter
Financial Instruction Following Evaluation (FIFE)
Glenn Matlin, Siddharth, Anirudh JM +3
Language Models (LMs) struggle with complex, interdependent instructions, particularly in high-stakes domains like finance where precision is critical. We introduce FIFE, a novel,…
Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
Rongzhi Zhang, Liqin Ye, Yuzhao Heng +5
Precise attribute intensity control--generating Large Language Model (LLM) outputs with specific, user-defined attribute intensities--is crucial for AI systems adaptable to diverse…
FinCap: Topic-Aligned Captions for Short-Form Financial YouTube Videos
Siddhant Sukhani, Yash Bhardwaj, Riya Bhadani +3
We evaluate multimodal large language models (MLLMs) for topic-aligned captioning in financial short-form videos (SVs) by testing joint reasoning over transcripts (T), audio (A), a…
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…
Finance Language Model Evaluation (FLaME)
Glenn Matlin, Mika Okamoto, Huzaifa Pardawala +2
Language Models (LMs) have demonstrated impressive capabilities with core Natural Language Processing (NLP) tasks. The effectiveness of LMs for highly specialized knowledge-intensi…
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…