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.

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.

How do we get students to show their reasoning in our math classroom community? On today's episode, I'm sharing about a simple way to build community & student confidence with a simple lesson plan straight from my favorite research based instructional practice... Word Problem Workshop!We are chatting about: What is a math community & why it matters The elements of the lesson plan How each element builds an essential skill for our students' Social Emotional Development!L...

🤖 Agent-as-a-Judge: Evaluate Agents with AgentsThe paper detail a new framework for evaluating agentic systems called Agent-as-a-Judge, which uses other agentic systems to assess their performance. To test this framework, the authors created DevAI, a benchmark dataset consisting of 55 realistic automated AI development tasks. They compared Agent-as-a-Judge to LLM-as-a-Judge and Human-as-a-Judge on DevAI, finding that Agent-as-a-Judge outperforms both, aligning closely with human evaluations. The

⚖️ First-Person Fairness in ChatbotsThis paper from OpenAI examines potential bias in chatbot systems like ChatGPT, specifically focusing on how a user's name, which can be associated with demographic attributes, influences the chatbot's responses. The authors propose a privacy-preserving method to measure user name bias across a large dataset of real-world chatbot interactions. They identify several instances of bias, demonstrating that chatbot responses can show a tendency towards creating

🤔 Thinking LLMs: General Instruction Following with Thought GenerationThis research paper explores the concept of "Thinking LLMs," or large language models that can generate internal thoughts before responding to user prompts. The authors propose a training method called Thought Preference Optimization (TPO) which uses an iterative process to encourage LLMs to develop thinking abilities. TPO leverages an existing judge model that evaluates responses, implicitly guiding the model to improve its

🔋 Addition is All You Need for Energy-efficient Language ModelsThis research paper introduces a novel algorithm called Linear-Complexity Multiplication (L-Mul) that aims to make language models more energy-efficient. L-Mul replaces computationally expensive floating-point multiplications with integer addition operations, significantly reducing energy consumption. The authors demonstrate that L-Mul achieves high precision, even surpassing 8-bit floating-point multiplications in certain cases. They

🤖 MLE-bench: Evaluating Machine Learning Agents on Machine Learning EngineeringThe paper introduces MLE-bench, a benchmark designed to evaluate AI agents' ability to perform machine learning engineering tasks. The benchmark comprises 75 Kaggle competitions, each requiring agents to solve real-world problems involving data preparation, model training, and code debugging. Researchers evaluated several cutting-edge language models on MLE-bench, with the best-performing setup achieving at least a

📈 Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAGThis paper explores the challenges and opportunities of using long-context language models (LLMs) in retrieval-augmented generation (RAG) systems. While increasing the number of retrieved passages initially improves performance, the authors find that it eventually degrades due to the introduction of irrelevant information, or "hard negatives." To address this, the paper proposes three methods for enhancing the robustness of

📊 GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language ModelsThe paper investigates the mathematical reasoning abilities of large language models (LLMs). The authors created a new benchmark, GSM-Symbolic, to test LLMs' performance in a more reliable way. The results show that LLMs perform poorly and inconsistently across different versions of the same question, indicating a fragility in their reasoning abilities. Additionally, the models are sensitive to

😶 Anti-Social Behavior and Persuasion Ability of LLMsThis study explores the behavior of Large Language Models (LLMs) in a simulated prison environment, inspired by the Stanford Prison Experiment. It focuses on two key aspects: persuasion, where a prisoner tries to convince a guard to grant more yard time or help escape, and anti-social behavior, such as toxicity and violence. The analysis reveals that some models, like Mixtral and Mistral, struggle to maintain their assigned roles. Persuasion is

🎧 Differential TransformerThe paper introduces the Differential Transformer, a new architecture for large language models (LLMs) that aims to improve their ability to focus on relevant information within long sequences. It achieves this by introducing a differential attention mechanism which calculates attention scores as the difference between two separate softmax attention maps, effectively canceling out noise and promoting sparse attention patterns. This enhanced focus on relevant context leads

🛠 ToolGen: Unified Tool Retrieval and Calling via GenerationThis research paper introduces ToolGen, a novel framework that enables LLMs to directly access and utilize external tools by representing each tool as a unique token within the model's vocabulary. ToolGen addresses the limitations of traditional tool retrieval methods, which often rely on separate retrieval mechanisms and are constrained by context length. The paper describes a three-stage training process for ToolGen, consisting of tool

👨‍🔧 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