We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. We begin by introducing key concepts, assumptions, and estimands within both the…
Large language models (LLMs) excel at few-shot in-context learning (ICL) — learning from a few examples provided in context at inference, without any weight updates.…
Large language models (LLMs) are increasing in capability and popularity, propelling their application in new domains — including as replacements for human participants in computational…
Real-world data for classification is often labeled by multiple annotators. For analyzing such data, we introduce CROWDLAB, a straightforward approach to utilize any trained classifier…
The indoctrination game is a complete-information contest over public opinion. The players exert costly effort to manifest their private opinions in public in order to…
Retrieval augmented generation (RAG) is often used to fix hallucinations and provide up-to-date knowledge for large language models (LLMs). However, in cases when the LLM…
We propose a theory for coupling matter fields with discrete geometry on higher-order networks, i.e. cell complexes. The key idea of the approach is to…
While artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen…
We introduce a novel framework, LM-Guided CoT, that leverages a lightweight (i.e., 10B) LM in reasoning tasks. Specifically, the…
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a…
Large Language Models (LLMs) have made significant strides in handling long sequences exceeding 32K tokens. However, their performance evaluation has largely been confined to metrics…
