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cs.CL2024
Persistent Topological Features in Large Language Models
Yuri Gardinazzi, Karthik Viswanathan, Giada Panerai +3
Understanding the decision-making processes of large language models is critical given their widespread applications. To achieve this, we aim to connect a formal mathematical frame…
cs.CL2024★ 4 cited
The representation landscape of few-shot learning and fine-tuning in large language models
Diego Doimo, Alessandro Serra, Alessio Ansuini +1
In-context learning (ICL) and supervised fine-tuning (SFT) are two common strategies for improving the performance of modern large language models (LLMs) on specific tasks. Despite…