147 citations · 502 across the 38 of their papers we have counts for
5 papers · 1 filter
Efficient multi-prompt evaluation of LLMs
Felipe Maia Polo, Ronald Xu, Lucas Weber +6
Most popular benchmarks for comparing LLMs rely on a limited set of prompt templates, which may not fully capture the LLMs' abilities and can affect the reproducibility of results…
Prompt Exploration with Prompt Regression
Michael Feffer, Ronald Xu, Yuekai Sun +1
In the advent of democratized usage of large language models (LLMs), there is a growing desire to systematize LLM prompt creation and selection processes beyond iterative trial-and…
Aligners: Decoupling LLMs and Alignment
Lilian Ngweta, Mayank Agarwal, Subha Maity +3
Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. Alignment is challenging, costly, and needs to be r…
tinyBenchmarks: evaluating LLMs with fewer examples
Felipe Maia Polo, Lucas Weber, Leshem Choshen +3
The versatility of large language models (LLMs) led to the creation of diverse benchmarks that thoroughly test a variety of language models' abilities. These benchmarks consist of…
Large Language Model Routing with Benchmark Datasets
Tal Shnitzer, Anthony Ou, Mírian Silva +5
There is a rapidly growing number of open-source Large Language Models (LLMs) and benchmark datasets to compare them. While some models dominate these benchmarks, no single model t…