471 citations · 611 across the 33 of their papers we have counts for
36 papers
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
Does Calibration Affect Human Actions?
Meir Nizri, Amos Azaria, Chirag Gupta +1
Calibration has been proposed as a way to enhance the reliability and adoption of machine learning classifiers. We study a particular aspect of this proposal: how does calibrating…
The Self-Execution Benchmark: Measuring LLMs' Attempts to Overcome Their Lack of Self-Execution
Elon Ezra, Ariel Weizman, Amos Azaria
Large language models (LLMs) are commonly evaluated on tasks that test their knowledge or reasoning abilities. In this paper, we explore a different type of evaluation: whether an…
TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages
Moshe Ofer, Orel Zamler, Amos Azaria
Large Language Models (LLMs) excel in high-resource languages but struggle with low-resource languages due to limited training data. This paper presents TALL (Trainable Architectur…
The Turing Test Is More Relevant Than Ever
Avraham Rahimov, Orel Zamler, Amos Azaria
The Turing Test, first proposed by Alan Turing in 1950, has historically served as a benchmark for evaluating artificial intelligence (AI). However, since the release of ELIZA in 1…
Fool Me, Fool Me: User Attitudes Toward LLM Falsehoods
Diana Bar-Or Nirman, Ariel Weizman, Amos Azaria
While Large Language Models (LLMs) have become central tools in various fields, they often provide inaccurate or false information. This study examines user preferences regarding f…
Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System
Robin Schmucker, Meng Xia, Amos Azaria +1
Conversational tutoring systems (CTSs) offer learning experiences through interactions based on natural language. They are recognized for promoting cognitive engagement and improvi…
Identifying the Source of Information Spread in Networks via Markov Chains
Yael Sabato, Amos Azaria, Noam Hazon
Nowadays, the diffusion of information through social networks is a powerful phenomenon. One common way to model diffusions in social networks is the Independent Cascade (IC) model…
Ruffle&Riley: Towards the Automated Induction of Conversational Tutoring Systems
Robin Schmucker, Meng Xia, Amos Azaria +1
Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interaction. They are known to promote high levels of cognitive engagement and benefit…
Performance of ChatGPT-3.5 and GPT-4 on the United States Medical Licensing Examination With and Without Distractions
Myriam Safrai, Amos Azaria
As Large Language Models (LLMs) are predictive models building their response based on the words in the prompts, there is a risk that small talk and irrelevant information may alte…
ChatGPT is a Remarkable Tool -- For Experts
Amos Azaria, Rina Azoulay, Shulamit Reches
This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains, including scientific writing, mathematics, education, programming, and healthcare.…
Coalition Formation with Bounded Coalition Size
Chaya Levinger, Noam Hazon, Sofia Simola +1
In many situations when people are assigned to coalitions, the utility of each person depends on the friends in her coalition. Additionally, in many situations, the size of each co…
Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents
Yue Wu, So Yeon Min, Yonatan Bisk +5
Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging co…
SPRING: Studying the Paper and Reasoning to Play Games
Yue Wu, Shrimai Prabhumoye, So Yeon Min +5
Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement le…
The Internal State of an LLM Knows When It's Lying
Amos Azaria, Tom Mitchell
While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a c…
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
Yue Wu, Yewen Fan, Paul Pu Liang +3
High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructu…
Meta-Reinforcement Learning Using Model Parameters
Gabriel Hartmann, Amos Azaria
In meta-reinforcement learning, an agent is trained in multiple different environments and attempts to learn a meta-policy that can efficiently adapt to a new environment. This pap…
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning
Yitzhak Spielberg, Amos Azaria
In the context of reinforcement learning we introduce the concept of criticality of a state, which indicates the extent to which the choice of action in that particular state influ…
Revelation of Task Difficulty in AI-aided Education
Yitzhak Spielberg, Amos Azaria
When a student is asked to perform a given task, her subjective estimate of the difficulty of that task has a strong influence on her performance. There exists a rich literature on…
The Concept of Criticality in AI Safety
Yitzhak Spielberg, Amos Azaria
When AI agents don't align their actions with human values they may cause serious harm. One way to solve the value alignment problem is by including a human operator who monitors a…
A Socially Aware Reinforcement Learning Agent for The Single Track Road Problem
Ido Shapira, Amos Azaria
We present the single track road problem. In this problem two agents face each-other at opposite positions of a road that can only have one agent pass at a time. We focus on the sc…
Competitive Driving of Autonomous Vehicles
Gabriel Hartmann, Zvi Shiller, Amos Azaria
This paper describes Ariel Team's autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race. IAC is the first multi-vehicle autonomous head-to-head compe…
Explaining Ridesharing: Selection of Explanations for Increasing User Satisfaction
David Zar, Noam Hazon, Amos Azaria
Transportation services play a crucial part in the development of modern smart cities. In particular, on-demand ridesharing services, which group together passengers with similar i…
Conversational Neuro-Symbolic Commonsense Reasoning
Forough Arabshahi, Jennifer Lee, Mikayla Gawarecki +3
In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much more commonsense, including the ability to identify unstated pre…