Large Language Models
Forecasting is a critical task in decision making across various domains. While numerical data provides a foundation, it often lacks …
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners …
Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data …
In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference …
This paper introduces a novel model compression approach through dynamic layer-specific pruning in Large Language Models (LLMs), …
Direct Preference Optimization (DPO) is an effective technique that leverages pairwise preference data (usually one chosen and rejected …
In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference …
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those …
Large decoder-only language models (LLMs) are the state-of-the-art models on most of today’s NLP tasks and benchmarks. Yet, the …
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on …