221 citations · 236 across the 7 of their papers we have counts for
7 papers
Transformers Can Achieve Length Generalization But Not Robustly
Yongchao Zhou, Uri Alon, Xinyun Chen +3
Length generalization, defined as the ability to extrapolate from shorter training sequences to longer test ones, is a significant challenge for language models. This issue persist…
In-Context Principle Learning from Mistakes
Tianjun Zhang, Aman Madaan, Luyu Gao +5
In-context learning (ICL, also known as few-shot prompting) has been the standard method of adapting LLMs to downstream tasks, by learning from a few input-output examples. Nonethe…
CAT-LM: Training Language Models on Aligned Code And Tests
Nikitha Rao, Kush Jain, Uri Alon +2
Testing is an integral part of the software development process. Yet, writing tests is time-consuming and therefore often neglected. Classical test generation tools such as EvoSuit…
Contextual Predictive Mutation Testing
Kush Jain, Uri Alon, Alex Groce +1
Mutation testing is a powerful technique for assessing and improving test suite quality that artificially introduces bugs and checks whether the test suites catch them. However, it…
GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models
Itzik Malkiel, Uri Alon, Yakir Yehuda +4
Transcriptions of phone calls are of significant value across diverse fields, such as sales, customer service, healthcare, and law enforcement. Nevertheless, the analysis of these…
Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan, Niket Tandon, Prakhar Gupta +13
Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an…