4 papers
Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
Zhiyuan Jerry Lin, Benjamin Letham, Samuel Dooley +2
System prompts are a central control mechanism in modern AI systems, shaping behavior across conversations, tasks, and user populations. Yet they are difficult to tune when feedbac…
LiveBench: A Challenging, Contamination-Limited LLM Benchmark
Colin White, Samuel Dooley, Manley Roberts +15
Test set contamination, wherein test data from a benchmark ends up in a newer model's training set, is a well-documented obstacle for fair LLM evaluation and can quickly render ben…
Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes
Mayuka Jayawardhana, Renbo, Samuel Dooley +6
Large language models (LLMs) perform remarkably well on tabular datasets in zero- and few-shot settings, since they can extract meaning from natural language column headers that de…
Multi-objective Differentiable Neural Architecture Search
Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler +3
Pareto front profiling in multi-objective optimization (MOO), i.e., finding a diverse set of Pareto optimal solutions, is challenging, especially with expensive objectives that req…