👨‍🔧 Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI ExpertsThis research proposes LangGPT, a structural prompt framework for designing prompts to instruct Large Language Models (LLMs). This framework, inspired by programming languages, provides a systematic and reusable method for creating prompts that are easier for non-AI experts to understand and use. The authors also present Minstrel, a multi-agent system that automatically generates these structural prompts,

🎞 Movie Gen: A Cast of Media Foundation ModelsMeta AI researchers have introduced Movie Gen, a suite of foundation models capable of generating high-quality video and audio. Movie Gen models can synthesize videos based on text prompts, personalize videos using a user’s image, precisely edit videos with text instructions, and generate synchronized audio for videos. The research paper details the models' architecture, training procedures, and evaluation results, demonstrating their superior

🕵️‍♀️ LLMs Know More Than They ShowThis research examines the inner workings of large language models (LLMs) to understand and reduce their tendency to generate false information, known as "hallucinations." The authors find that LLMs internally encode information about the truthfulness of their outputs, with these signals concentrated in tokens related to exact answers. However, these truth signals are task-specific and may not apply universally across different tasks. They also find that LLMs'

🔁 Were RNNs All We Needed?The paper "Were RNNs All We Needed?" examines the efficiency of traditional recurrent neural networks (RNNs), specifically LSTMs and GRUs, for long sequences. The authors demonstrate that by removing hidden state dependencies from their input, forget, and update gates, LSTMs and GRUs can be trained efficiently using the parallel prefix scan algorithm, resulting in significantly faster training times. They introduce simplified versions of these RNNs, called minLSTMs and

💊 A Preliminary Study of o1 in MedicineThe research paper focuses on the performance of a new large language model (LLM) called o1 in the medical domain. o1 was trained with an internalized chain-of-thought technique using reinforcement learning strategies, which enhances its reasoning abilities. The paper evaluates o1 across three key aspects: understanding, reasoning, and multilinguality, using a diverse range of medical datasets. The researchers found that o1 demonstrates improved understanding

📑 RAG and BeyondThis paper provides a comprehensive survey of the current state of data-augmented Large Language Models (LLMs), focusing on Retrieval-Augmented Generation (RAG) and beyond. The authors classify different types of queries that utilize external data into four levels based on their complexity: explicit fact queries, implicit fact queries, interpretable rationale queries, and hidden rationale queries. They discuss the specific challenges associated with each level and provide a detailed

🔓 Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal ModelsThis research paper introduces Molmo, a new family of vision-language models (VLMs) that surpasses existing open-weight models in performance while maintaining open weights, data, and code. The key innovation is the collection of a large, detailed image caption dataset using speech-based descriptions, avoiding reliance on synthetic data generated by proprietary VLMs. Molmo is trained on this dataset, along with a

🔄 Self-Taught EvaluatorsThis research paper explores the development of self-taught language model evaluators. Instead of relying on costly human annotations, this approach utilizes synthetic data generated by the model itself. The method iteratively trains an LLM-as-a-Judge by creating contrasting response pairs, generating reasoning traces, and fine-tuning the model on this synthetic data. The research demonstrates that this method significantly improves the accuracy of the evaluator on

⚠️ Larger and more instructable language models become less reliableThis research paper from Nature explores the relationship between the size and instructability of large language models (LLMs) and their reliability. The study finds that while larger, more instructable LLMs tend to perform better on complex tasks, they become less reliable in handling simple tasks, often producing plausible but incorrect answers instead of safely avoiding them. Additionally, the study highlights the limitations of

💭 Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in LLMsThis research paper introduces Logic-of-Thought (LoT), a novel prompting method designed to enhance logical reasoning in large language models. LoT extracts propositions and logical relations from input text, expands them using logical rules, and reintegrates this information into the original prompt. Unlike existing techniques, LoT preserves information and guides the model's reasoning process while leveraging its natural

🟢 Moshi: a speech-text foundation model for real-time dialogueThe paper discusses a new multimodal foundation model called Moshi designed for real-time, full-duplex spoken dialogue. This model uses a text-based LLM called Helium to provide reasoning abilities and a neural audio codec called Mimi to encode audio into tokens. Moshi is innovative because it can handle overlapping speech and model both the user's and the system's speech in a single stream. The paper also explores the model's

🔑 Jailbreaking Large Language Models with Symbolic MathematicsThis research paper investigates a new vulnerability in AI safety mechanisms by introducing MathPrompt, a technique that utilizes symbolic mathematics to bypass LLM safety measures. The paper demonstrates that encoding harmful natural language prompts into mathematical problems allows LLMs to generate harmful content, despite being trained to prevent it. Experiments across 13 state-of-the-art LLMs show a high success rate for MathPrompt,

📈 LLMs Still Can't Plan; Can LRMs?The paper "LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench" investigates the ability of large language models (LLMs) to plan, using a benchmark called PlanBench. The authors find that while OpenAI's new "Large Reasoning Model" (LRM) o1 shows significant improvement in planning abilities, it still falls short of fully achieving the task. This research highlights the need for further investigation into the accuracy, efficiency,

📏 A Comprehensive Evaluation of Quantized Instruction-Tuned LLMsThis paper, titled "A Comprehensive Evaluation of Quantized Instruction-Tuned Large Language Models: An Experimental Analysis up to 405B," examines the performance of large language models (LLMs) after they have been compressed using various quantization methods. The authors assess the impact of these techniques on different task types and model sizes, including the very large 405B parameter Llama 3.1 model. They explore how different

An AI-generated short discussion of an Illustrative Mathematics lesson to help educators prepare to teach it. The episode is intended to cover: the big mathematical ideas in the lessonthe main activities students dohow to make it interesting for young peoplepossible misconceptions and how to deal with them.

An AI-generated short discussion of an Illustrative Mathematics lesson to help educators prepare to teach it. The episode is intended to cover: the big mathematical ideas in the lessonthe main activities students dohow to make it interesting for young peoplepossible misconceptions and how to deal with them.

An AI-generated short discussion of an Illustrative Mathematics lesson to help educators prepare to teach it. The episode is intended to cover: the big mathematical ideas in the lessonthe main activities students dohow to make it interesting for young peoplepossible misconceptions and how to deal with them.

An AI-generated short discussion of an Illustrative Mathematics lesson to help educators prepare to teach it. The episode is intended to cover: the big mathematical ideas in the lessonthe main activities students dohow to make it interesting for young peoplepossible misconceptions and how to deal with them.

An AI-generated short discussion of an Illustrative Mathematics lesson to help educators prepare to teach it. The episode is intended to cover: the big mathematical ideas in the lessonthe main activities students dohow to make it interesting for young peoplepossible misconceptions and how to deal with them.

🧠 On the Diagram of ThoughtThis paper introduces a new framework called Diagram of Thought (DoT) that models how large language models (LLMs) reason. Unlike traditional methods that represent reasoning as linear chains or trees, DoT utilizes a directed acyclic graph (DAG) structure. This structure allows LLMs to navigate complex reasoning pathways while ensuring logical consistency. By incorporating feedback mechanisms and leveraging auto-regressive next-token prediction, DoT enables LLMs to