🌸 Agents

🌼 The AI Scientist: Towards Fully AutomatedOpen-Ended Scientific Discovery 🧑‍🔬

🌻 The AI Scientist's Four Main Processes

• Idea Generation:

  • Starts with a given code template related to an existing topic.

  • "Brainstorms" a diverse set of novel research directions based on the template.

  • Uses Semantic Scholar to ensure the novelty of its ideas.

• Experimental Iteration:

  • Executes proposed experiments based on the generated idea and template.

  • Produces plots to visualize results and makes notes describing the content of each plot.

• Paper Write-up:

  • Produces a concise and informative write-up in the style of a standard machine learning conference proceeding using LaTeX.

  • Autonomously finds relevant papers to cite using Semantic Scholar.

• Automated Paper Reviewing:

  • Develops an automated LLM-powered reviewer capable of evaluating generated papers with near-human accuracy.

  • Generated reviews can be used to improve the project or provide feedback for future iterations, enabling continuous improvement.

🌻Generated Paper:

• Unlocking Grokking: A Comparative Study of Weight Initialization Strategies in Transformer Models

• Adaptive Learning Rates for Transformers via Q-Learning

• StyleFusion: Adaptive Multi-style Generation in Character-Level Language Models

🌻Code

🌼 Can LLMs Beat Humans in Debating?A Dynamic Multi-agent Framework for Competitive Debate

🌻 Competitive Debate Challenges for LLMs:

• Competitive debate is a complex computational argumentation task.

• Large Language Models (LLMs) struggle with hallucinations and lack competitiveness in this domain.

🌻Agent Roles:

• Searcher: Conducts initial research to gather information.

• Analyzer: Formulates arguments based on the research.

• Writer: Composes the debate content, including rebuttals and summaries.

• Reviewer: Evaluates and refines the debate content.

🌻Code

🌼 Enhancing the Code Debugging Ability of LLMs via Communicative Agent Based Data Refinement

🌻 Introduction of DEBUGEVAL:

• DEBUGEVAL is a comprehensive benchmark designed to evaluate the debugging capabilities of LLMs.

• It collects data from high-quality datasets and designs four tasks to assess debugging effectiveness: BUG Localization, BUG Identification, Code Review, and Code Repair.

🌻 Introduction of MASTER Framework

• MASTER (CoMmunicative Agent BaSed DaTa REfinement FRamework) is proposed to enhance LLMs' code debugging abilities by generating refined debugging data for supervised fine-tuning.

• MASTER employs three agents:

  • Code Quizzer: Generates refined data according to DEBUGEVAL tasks.

  • Code Learner: Acts as a critic, reserving problems it cannot solve.

  • Code Teacher: Provides detailed Chain-of-Thought based solutions to the generated problems.

• The synthesized data is used to finetune the Code Learner, leading to the development of the NeuDebugger model.

🌻 Experimental Results:

• Experiments on DEBUGEVAL show that 7B-scale LLMs have weaker debugging capabilities, even those designed for code.

• Larger models (over 70B) exhibit more convincing debugging abilities.

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