221 citations · 1.1k across the 24 of their papers we have counts for
8 papers · 1 filter
WebArena: A Realistic Web Environment for Building Autonomous Agents
Shuyan Zhou, Frank F. Xu, Hao Zhu +9
With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and…
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…
On the Expressivity Role of LayerNorm in Transformers' Attention
Shaked Brody, Uri Alon, Eran Yahav
Layer Normalization (LayerNorm) is an inherent component in all Transformer-based models. In this paper, we show that LayerNorm is crucial to the expressivity of the multi-head att…
Unlimiformer: Long-Range Transformers with Unlimited Length Input
Amanda Bertsch, Uri Alon, Graham Neubig +1
Since the proposal of transformers, these models have been limited to bounded input lengths, because of their need to attend to every token in the input. In this work, we propose U…
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…
Learning Performance-Improving Code Edits
Alexander Shypula, Aman Madaan, Yimeng Zeng +7
With the decline of Moore's law, optimizing program performance has become a major focus of software research. However, high-level optimizations such as API and algorithm changes r…
CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code
Shuyan Zhou, Uri Alon, Sumit Agarwal +1
Since the rise of neural natural-language-to-code models (NL->Code) that can generate long expressions and statements rather than a single next-token, one of the major problems has…
Why do Nearest Neighbor Language Models Work?
Frank F. Xu, Uri Alon, Graham Neubig
Language models (LMs) compute the probability of a text by sequentially computing a representation of an already-seen context and using this representation to predict the next word…