3 citations · 9 across the 19 of their papers we have counts for
6 papers · 1 filter
A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation
Bhaskarjit Sarmah, Kriti Dutta, Anna Grigoryan +3
We argue that the Declarative Self-improving Python (DSPy) optimizers are a way to align the large language model (LLM) prompts and their evaluations to the human annotations. We p…
How to Choose a Threshold for an Evaluation Metric for Large Language Models
Bhaskarjit Sarmah, Mingshu Li, Jingrao Lyu +4
To ensure and monitor large language models (LLMs) reliably, various evaluation metrics have been proposed in the literature. However, there is little research on prescribing a met…
Can an unsupervised clustering algorithm reproduce a categorization system?
Nathalia Castellanos, Dhruv Desai, Sebastian Frank +2
Peer analysis is a critical component of investment management, often relying on expert-provided categorization systems. These systems' consistency is questioned when they do not a…
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals
Gregory Yampolsky, Dhruv Desai, Mingshu Li +2
The explainability of black-box machine learning algorithms, commonly known as Explainable Artificial Intelligence (XAI), has become crucial for financial and other regulated indus…
HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction
Bhaskarjit Sarmah, Benika Hall, Rohan Rao +3
Extraction and interpretation of intricate information from unstructured text data arising in financial applications, such as earnings call transcripts, present substantial challen…
Open Set Recognition for Random Forest
Guanchao Feng, Dhruv Desai, Stefano Pasquali +1
In many real-world classification or recognition tasks, it is often difficult to collect training examples that exhaust all possible classes due to, for example, incomplete knowled…