Danny Karmon

ComMer: a Framework for Compressing and Merging User Data for Personalization

Yoel Zeldes, Amir Zait, Ilia Labzovsky, Danny Karmon et autres

Large Language Models (LLMs) excel at a wide range of tasks, but adapting them to new data, particularly for personalized applications, poses significant challenges due to resource and computational constraints. Existing methods either rely on exposing fresh data to the model through …

Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia

Chandler Smith, Marwa Abdulhai, Manfred Diaz, Marko Tesic et autres

Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with both human and artificial agents. These interactions represent a critical frontier for LLM-based agents, yet existing evaluation methods …

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Evidence of Cognitive Deficits andDevelopmental Advances in Generative AI: A Clock Drawing Test Analysis

Isaac R. Galatzer‐Levy, Jed N. McGiffin, David Munday, Xin Liu et autres

Generative AI's rapid advancement sparks interest in its cognitive abilities, especially given its capacity for tasks like language understanding and code generation. This study explores how several recent GenAI models perform on the Clock Drawing Test (CDT), a neuropsychological assessment of visuospatial …

The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks

Isaac R. Galatzer‐Levy, David Munday, Jed N. McGiffin, Xin Liu et autres

There is increasing interest in tracking the capabilities of general intelligence foundation models. This study benchmarks leading large language models and vision language models against human performance on the Wechsler Adult Intelligence Scale (WAIS-IV), a comprehensive, population-normed assessment of underlying human cognition …

The Ability of Large Language Models to Evaluate Constraint-satisfaction in Agent Responses to Open-ended Requests

Lior Madmoni, Amir Zait, Ilia Labzovsky, Danny Karmon

Generative AI agents are often expected to respond to complex user requests that have No One Right Answer (NORA), e.g., "design a vegetarian meal plan below 1800 calories". Such requests may entail a set of constraints that the agent should adhere to. …

Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia

Alexander Sasha Vezhnevets, John Agapiou, Avia Aharon, Ron Ziv et autres

Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as it absorbs the new affordances provided by Large Language Models (LLM)s. Generative Agent-Based …

Federated Multilingual Models for Medical Transcript Analysis

Andre Manoel, Mirian Hipolito Garcia, Tal Baumel, Shize Su et autres

Federated Learning (FL) is a novel machine learning approach that allows the model trainer to access more data samples, by training the model across multiple decentralized data sources, while data access constraints are in place. Such trained models can achieve significantly higher …

Algorithmic copywriting: automated generation of health-related advertisements to improve their performance

Brit Youngmann, Elad Yom‐Tov, Ran Gilad-Bachrach, Danny Karmon

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Insecticides, biologics and nematicides: Updates to IRAC’s mode of action classification - a tool for resistance management

Thomas C. Sparks, Andrew J. Crossthwaite, Ralf Nauen, Shinichi Banba et autres

Insecticide resistance has been and continues to be a significant problem for invertebrate pest control. As such, effective insecticide resistance management (IRM) is critical to maintain the efficacy of current and future insecticides. A technical group within CropLife International, the Insecticide Resistance …

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The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance

Brit Youngmann, Elad Yom‐Tov, Ran Gilad-Bachrach, Danny Karmon

Search advertising is one of the most commonly-used methods of advertising. Past work has shown that search advertising can be employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and (possible expensive) experimentation, both of which …

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Algorithmic Copywriting: Automated Generation of Health-Related\n Advertisements to Improve their Performance

Brit Youngmann, Ran Gilad-Bachrach, Danny Karmon, Elad Yom‐Tov

Search advertising, a popular method for online marketing, has been employed\nto improve health by eliciting positive behavioral change. However, writing\neffective advertisements requires expertise and experimentation, which may not\nbe available to health authorities wishing to elicit such changes, especially\nwhen dealing with public health …

LaVAN: Localized and Visible Adversarial Noise

Danny Karmon, Daniel Zoran, Yoav Goldberg

Most works on adversarial examples for deep-learning based image classifiers use noise that, while small, covers the entire image. We explore the case where the noise is allowed to be visible but confined to a small, localized patch of the image, without …