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cs.IR2024
Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsing
Daking Rai, Rydia R. Weiland, Kayla Margaret Gabriella Herrera +2
Explaining the decisions of AI has become vital for fostering appropriate user trust in these systems. This paper investigates explanations for a structured prediction task called…
cs.AI2024★ 12 cited
A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models
Daking Rai, Yilun Zhou, Shi Feng +2
Mechanistic interpretability (MI) is an emerging sub-field of interpretability that seeks to understand a neural network model by reverse-engineering its internal computations. Rec…
cs.AI2024
An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs
Daking Rai, Ziyu Yao
Large language models (LLMs) have shown strong arithmetic reasoning capabilities when prompted with Chain-of-Thought (CoT) prompts. However, we have only a limited understanding of…