24 citations · 27 across the 3 of their papers we have counts for
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cs.LG2024
Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference
Jorge García-Carrasco, Alejandro Maté, Juan Trujillo
Large Language Models (LLMs) have shown impressive performance across a wide range of tasks. However, the size of LLMs is steadily increasing, hindering their application on comput…
cs.LG2024★ 3 cited
How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability
Jorge García-Carrasco, Alejandro Maté, Juan Trujillo
Transformer-based language models are treated as black-boxes because of their large number of parameters and complex internal interactions, which is a serious safety concern. Mecha…