Large language models (LLMs) have shown remarkable reasoning capabilities, especially
when prompted to generate intermediate reasoning steps (eg, Chain-of-Thought, CoT) …

Recently an influx of studies claims emergent cognitive abilities in large language models
(LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack …

Highly effective, task-specific prompts are often heavily engineered by experts to integrate
detailed instructions and domain insights based on a deep understanding of both instincts of …

We investigate the extent to which Large Language Models (LLMs) can simulate the
execution of computer code and algorithms. We begin by looking straight line programs, and …

Since their inception, programming languages have trended towards greater readability and
lower barriers for programmers. Following this trend, natural language can be a promising …

We introduce the AutoGRAMS framework for programming multi-step interactions with
language models. AutoGRAMS represents AI agents as a graph, where each node can …

Operational decisions such as loan or subsidy allocation are taken with high frequency and
require a consistent decision quality which decision models can ensure. Decision models …

Executing computer programs described in natural language has long been a pursuit of
computer science. With the advent of enhanced natural language understanding capabilities …

Recent advancements in Large Language Models (LLMs) have shown significant progress
in understanding complex natural language. One important application of LLM is LLM-based …

The ability to provide clear and transparent explanations for the outcome of a decision is
critical for gaining user trust and acceptance, particularly in areas such as healthcare …