1 citations · 1 across the 5 of their papers we have counts for
5 papers
Measuring What LLMs Think They Do: SHAP Faithfulness and Deployability on Financial Tabular Classification
Saeed AlMarri, Mathieu Ravaut, Kristof Juhasz +3
Large Language Models (LLMs) have attracted significant attention for classification tasks, offering a flexible alternative to trusted classical machine learning models like LightG…
Interpreting LLMs as Credit Risk Classifiers: Do Their Feature Explanations Align with Classical ML?
Saeed AlMarri, Kristof Juhasz, Mathieu Ravaut +3
Large Language Models (LLMs) are increasingly explored as flexible alternatives to classical machine learning models for classification tasks through zero-shot prompting. However,…
StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs
Hailin Chen, Fangkai Jiao, Mathieu Ravaut +8
The rapid advancement of large language models (LLMs) demands robust, unbiased, and scalable evaluation methods. However, human annotations are costly to scale, model-based evaluat…
Enriching Datasets with Demographics through Large Language Models: What's in a Name?
Khaled AlNuaimi, Gautier Marti, Mathieu Ravaut +3
Enriching datasets with demographic information, such as gender, race, and age from names, is a critical task in fields like healthcare, public policy, and social sciences. Such de…
Parameter-Efficient Conversational Recommender System as a Language Processing Task
Mathieu Ravaut, Hao Zhang, Lu Xu +2
Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation. Prior work often utilizes exte…