18 citations · 39 across the 18 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…