17 citations · 43 across the 8 of their papers we have counts for
13 papers
Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models
Melkamu Abay Mersha, Jugal Kalita
Transformer models achieve state-of-the-art performance across domains and tasks, yet their deeply layered representations make their predictions difficult to interpret. Existing e…
Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions
Smita Khapre, Melkamu Abay Mersha, Hassan Shakil +2
The evolution of digital communication systems and the designs of online platforms have inadvertently facilitated the subconscious propagation of toxic behavior. Giving rise to rea…
Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming
Melkamu Abay Mersha, Jugal Kalita
Virtual brainstorming sessions have become a central component of collaborative problem solving, yet the large volume and uneven distribution of ideas often make it difficult to ex…
Explainable AI: XAI-Guided Context-Aware Data Augmentation
Melkamu Abay Mersha, Mesay Gemeda Yigezu, Atnafu Lambebo Tonja +4
Explainable AI (XAI) has emerged as a powerful tool for improving the performance of AI models, going beyond providing model transparency and interpretability. The scarcity of labe…
A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMs
Melkamu Abay Mersha, Mesay Gemeda Yigezu, Hassan Shakil +3
The increasing complexity of LLMs presents significant challenges to their transparency and interpretability, necessitating the use of eXplainable AI (XAI) techniques to enhance tr…
Evaluating the Effectiveness of XAI Techniques for Encoder-Based Language Models
Melkamu Abay Mersha, Mesay Gemeda Yigezu, Jugal Kalita
The black-box nature of large language models (LLMs) necessitates the development of eXplainable AI (XAI) techniques for transparency and trustworthiness. However, evaluating these…