37 citations · 61 across the 10 of their papers we have counts for
10 papers
Has My System Prompt Been Used? Large Language Model Prompt Membership Inference
Roman Levin, Valeriia Cherepanova, Abhimanyu Hans +2
Prompt engineering has emerged as a powerful technique for optimizing large language models (LLMs) for specific applications, enabling faster prototyping and improved performance,…
Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges
Nayoung Lee, Ziyang Cai, Avi Schwarzschild +2
Large language models often struggle with length generalization and solving complex problem instances beyond their training distribution. We present a self-improvement approach whe…
The CLRS-Text Algorithmic Reasoning Language Benchmark
Larisa Markeeva, Sean McLeish, Borja Ibarz +7
Eliciting reasoning capabilities from language models (LMs) is a critical direction on the path towards building intelligent systems. Most recent studies dedicated to reasoning foc…
Benchmarking ChatGPT on Algorithmic Reasoning
Sean McLeish, Avi Schwarzschild, Tom Goldstein
We evaluate ChatGPT's ability to solve algorithm problems from the CLRS benchmark suite that is designed for GNNs. The benchmark requires the use of a specified classical algorithm…
TOFU: A Task of Fictitious Unlearning for LLMs
Pratyush Maini, Zhili Feng, Avi Schwarzschild +2
Large language models trained on massive corpora of data from the web can memorize and reproduce sensitive or private data raising both legal and ethical concerns. Unlearning, or t…
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Neel Jain, Ping-yeh Chiang, Yuxin Wen +10
We show that language model finetuning can be improved, sometimes dramatically, with a simple augmentation. NEFTune adds noise to the embedding vectors during training. Standard fi…