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 significantly increases the rate of materials discovery, patent filings, and product innovation, but these benefits are unequally distributed among scientists. The researchers find that the AI tool is most beneficial to scientists with strong judgment skills, which involve the ability to evaluate and prioritize AI-generated candidate compounds. The study also reveals that the AI tool automates a significant portion of idea generation tasks, resulting in a reallocation of scientist labor towards judgment tasks. This reallocation, along with the increased demand for judgment skills, explains the heterogeneous impact of the AI tool on scientific performance.

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 low-scoring outputs for preference optimization. Extensive experiments on several leading LLMs demonstrate that SEALONG effectively improves the long-context reasoning capabilities of LLMs without relying on human annotations or advanced models. The paper further analyzes the impact of various prompting strategies, scoring methods, and training parameters on SEALONG's performance.

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 preference), techniques (RAG, prompting, representation learning, and RLHF), evaluation metrics (intrinsic and extrinsic), and datasets (with and without ground-truth text). The paper concludes by highlighting key challenges for the future of personalized LLMs, including the cold-start problem, stereotype and bias issues, privacy concerns, and the complexities of multimodality.

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 complexity and input length increase. The authors also explore various techniques to improve NUPA, including specialized tokenizers, positional encodings, and data formats. Despite some successes in improving NUPA during pre-training, these techniques are found to be ineffective when applied to already trained models. The paper concludes that further research is necessary to address the challenges of NUPA in LLMs and enable them to confidently handle numerical tasks in real-world applications.

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 inspired by the Nominal Group Technique, a human-designed decision-making framework. The authors demonstrate that Multi-expert Prompting significantly outperforms existing prompting methods, especially in scenarios where diverse perspectives are valuable, and surpasses prior methods on various benchmarks. The paper also discusses ethical considerations related to the potential for bias amplification and explores ways to mitigate these risks.

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 combine their predictions. The authors demonstrate that MOICL outperforms traditional ICL and other baselines in classification tasks across various datasets, achieving higher accuracy while being more robust to noisy data and label imbalance. They also show that MOICL is more data and computationally efficient, making it a promising approach for improving the effectiveness of ICL.

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 significant individual progress by acquiring Minecraft items and that they can form meaningful relationships in large groups, demonstrating social understanding. Additionally, they explore the concept of civilizational progress through benchmarks that measure agent specialization into distinct professions, adherence to collective rules, and cultural transmission through memes and religion. The report concludes by discussing limitations of the current system and outlining areas for future research.

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 between concepts; the “brain” scale, where they identify clusters of features that tend to fire together within a document and are spatially localized; and the "galaxy" scale, where they examine the overall shape and clustering of the feature space. The authors find that the concept universe exhibits a surprising degree of structure, suggesting that SAEs can be a powerful tool for understanding the inner workings of LLMs.

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 model-specific datasets to identify these different types of hallucinations. The paper demonstrates that LLMs’ internal states can be used to distinguish between these two types of hallucinations, and that model-specific datasets are more effective for detecting HK+ hallucinations compared to generic datasets. The study highlights the importance of understanding and mitigating these different types of hallucinations to improve the reliability and accuracy of LLMs.

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 compression. The paper goes on to analyze the applications of SLMs in various NLP tasks, such as question answering, coding, and web search. Finally, the authors address the trustworthiness of SLMs and identify several promising future research directions.