The Daily ML

This research paper examines the impact of an artificial intelligence tool for materials discovery on the productivity and performance of scientists working in a large U.S. firm's R&D lab. The study exploits a randomized rollout of the AI tool across teams of scientists, allowing the researchers to draw causal inferences about the effects of the technology. The paper demonstrates that the AI tool...

This research paper investigates how large language models (LLMs) can improve their ability to reason over long contexts. The authors propose a self-improvement method called SEALONG that involves sampling multiple reasoning outputs from an LLM, scoring these outputs using Minimum Bayes Risk (MBR), and then fine-tuning the model using the highest-scoring outputs or by contrasting high-scoring and...

This paper is a survey of personalized large language models (LLMs), outlining different ways to adapt these models for user-specific needs. It analyzes how to personalize LLMs based on various user-specific data such as static attributes, interaction history, and pair-wise human preferences. The authors propose taxonomies for personalization granularity (user-level, persona-level, and global pref...

This research paper investigates the numerical understanding and processing abilities (NUPA) of large language models (LLMs). The authors introduce a benchmark, covering various numerical representations and tasks, to systematically evaluate LLMs' capabilities in handling numbers. The paper finds that while LLMs perform well on simpler tasks, their performance deteriorates significantly as task co...

This paper describes a novel method called Multi-expert Prompting that aims to improve the reliability, safety, and usefulness of large language models (LLMs). The method simulates multiple experts with different areas of expertise and aggregates their responses to a query, ultimately selecting the best answer based on criteria like truthfulness, factuality, and informativeness. This process is in...

The provided text describes a novel approach to in-context learning (ICL) called Mixtures of In-Context Learners (MOICL) that addresses key limitations of traditional ICL, such as context length constraints and sensitivity to noisy or out-of-distribution demonstrations. MOICL partitions a set of demonstrations into subsets, trains each subset as an "expert," and learns a weighting function to comb...

This technical report describes "Project Sid," an experiment that aims to create and study AI civilizations within a Minecraft environment. The researchers introduce a new cognitive architecture called PIANO, designed to enable agents to interact with each other and their environment in real-time while maintaining coherence across multiple output streams. They show that agents using PIANO can make...

This research paper investigates the structure of the concept universe represented by large language models (LLMs), specifically focusing on how sparse autoencoders (SAEs) can be used to discover and analyze concepts within these models. The authors explore this structure at three distinct scales: the “atomic” scale, where they look for geometric patterns representing semantic relationships betwee...

This research paper investigates the phenomenon of hallucinations in large language models (LLMs), focusing on distinguishing between two types: hallucinations caused by a lack of knowledge (HK-) and hallucinations that occur despite the LLM having the necessary knowledge (HK+). The authors introduce a novel methodology called WACK (Wrong Answers despite having Correct Knowledge), which constructs...

This paper provides a comprehensive survey of small language models (SLMs) in the context of large language models (LLMs). The authors discuss the benefits of SLMs over LLMs, including their low inference latency, cost-effectiveness, and ease of customization. They also explore the various techniques used to develop and enhance SLMs, including architecture design, training methods, and model compr...