32 citations · 35 across the 6 of their papers we have counts for
7 papers
Reinforcing Agentic Creativity in Scientific Ideation with Night Science
Priyanka Kargupta, Silviu Cucerzan, Shweti Mahajan +4
Large language models (LLMs) excel at structured, verifiable tasks, but their low-entropy bias can produce homogeneous and predictable outputs, limiting their utility for open-ende…
AgentInstruct: Toward Generative Teaching with Agentic Flows
Arindam Mitra, Luciano Del Corro, Guoqing Zheng +11
Synthetic data is becoming increasingly important for accelerating the development of language models, both large and small. Despite several successful use cases, researchers also…
Orca 2: Teaching Small Language Models How to Reason
Arindam Mitra, Luciano Del Corro, Shweti Mahajan +12
Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2…
Automatic Pair Construction for Contrastive Post-training
Canwen Xu, Corby Rosset, Ethan C. Chau +6
Alignment serves as an important step to steer large language models (LLMs) towards human preferences. In this paper, we propose an automatic way to construct contrastive data for…
Lexi: Self-Supervised Learning of the UI Language
Pratyay Banerjee, Shweti Mahajan, Kushal Arora +2
Humans can learn to operate the user interface (UI) of an application by reading an instruction manual or how-to guide. Along with text, these resources include visual content such…
Leveraging semantically similar queries for ranking via combining representations
Hayden S. Helm, Marah Abdin, Benjamin D. Pedigo +8
In modern ranking problems, different and disparate representations of the items to be ranked are often available. It is sensible, then, to try to combine these representations to…
Learning without gradient descent encoded by the dynamics of a neurobiological model
Vivek Kurien George, Vikash Morar, Weiwei Yang +6
The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss functi…