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cs.LG2026
Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls
Shubhangi Upasani, Chen Wu, Jay Rainton +4
Test-time adaptation enables large language models (LLMs) to modify their behavior at inference without updating model parameters. A common approach is many-shot prompting, where l…
cs.LG2024
Composition of Experts: A Modular Compound AI System Leveraging Large Language Models
Swayambhoo Jain, Ravi Raju, Bo Li +8
Large Language Models (LLMs) have achieved remarkable advancements, but their monolithic nature presents challenges in terms of scalability, cost, and customization. This paper int…
cs.LG2024★ 1 cited
Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge
Ravi Raju, Swayambhoo Jain, Bo Li +2
Large Language Models (LLMs) have revolutionized the landscape of machine learning, yet current benchmarks often fall short in capturing the diverse behavior of these models in rea…