471 citations · 611 across the 33 of their papers we have counts for
4 papers · 1 filter
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…