May I ask a follow-up question? Understanding the benefits of conversations inneural network explainability
Research in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, which cannot cater to the diverse backgrounds and understanding levels of users. With this paper, we investigate if free-fo...
Saved in:
Main Authors: | Zhang, Tong, Yang, Jessie X., Li, Boyang |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/180616 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Towards explainable artificial intelligence in the banking sector
by: Jew, Clarissa Bella
Published: (2024) -
Demystifying AI: bridging the explainability gap in LLMs
by: Chan, Darren Inn Siew
Published: (2024) -
Explainable AI for medical over-investigation identification
by: Suresh Kumar Rathika
Published: (2024) -
Building more explainable artificial intelligence with argumentation
by: Zeng, Zhiwei, et al.
Published: (2020) -
TeLLMe what you see: using LLMs to explain neurons in vision models
by: Guertler, Leon
Published: (2024)