3 citations · 3 across the 9 of their papers we have counts for
3 papers · 2 filters
Does Using Counterfactual Help LLMs Explain Textual Importance in Classification?
Nelvin Tan, James Asikin Cheung, Yu-Ching Shih +2
Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and large model sizes. More recently, the…
Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders
Shun Wang, Tyler Loakman, Youbo Lei +5
Large Language Models (LLMs) are traditionally viewed as black-box algorithms, therefore reducing trustworthiness and obscuring potential approaches to increasing performance on do…
Improved LLM Agents for Financial Document Question Answering
Nelvin Tan, Zian Seng, Liang Zhang +3
Large language models (LLMs) have shown impressive capabilities on numerous natural language processing tasks. However, LLMs still struggle with numerical question answering for fi…