30 citations · 89 across the 21 of their papers we have counts for
4 papers · 1 filter
optimize_anything: A Universal API for Optimizing any Text Parameter
Lakshya A Agrawal, Donghyun Lee, Shangyin Tan +11
Can a single LLM-based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are formulated as improving a tex…
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation
Alan Zhu, Parth Asawa, Jared Quincy Davis +5
As the demand for high-quality data in model training grows, researchers and developers are increasingly generating synthetic data to tune and train LLMs. However, current data gen…
RAFT: Adapting Language Model to Domain Specific RAG
Tianjun Zhang, Shishir G. Patil, Naman Jain +4
Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additi…
Rethinking Benchmark and Contamination for Language Models with Rephrased Samples
Shuo Yang, Wei-Lin Chiang, Lianmin Zheng +2
Large language models are increasingly trained on all the data ever produced by humans. Many have raised concerns about the trustworthiness of public benchmarks due to potential co…