Category
381 papers
A novel multi-agent AI model is introduced that aims to assess and compare the performance of various LLMs, and initial results indicate that the GPT-3.5 Turbo model's performance is comparatively better than the other models.
It is shown in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars.
The results indicate that if the OpenAPI specification is kept small and focused, LLM-based multi-agent systems are capable of generating complete functional code with business logic that aligns to the specification.
This work proposes and develops a multi-model unified platform to generate and execute code based on natural language prompts and presents practitioners feedback and insights into the use of LLMs in software development, including their strengths and weaknesses, key aspects overlooked by benchmarks and metrics.
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A system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices and assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages is presented.
An LLM-based multi-agent system is indicated that an LLM-based multi-agent system is a capable solution to update components of a legacy application autonomously.
A large language models based multi-agent system enables precise task execution and inter-agent collaboration, addressing the challenges of refactoring in functional programming.
This study presents a comprehensive empirical evaluation of 20 open-source Small Language Models and reveals that several compact SLMs achieve competitive results while maintaining a balance between performance and efficiency, making them viable for deployment in resource-constrained environments.
An LLM-based multi-agent system that autonomously upgrades legacy web applications to the latest versions and maintains context across tasks and agents, improving solution quality over the base model in some cases is proposed.
This article analyzes the flow-debt tradeoffs associated with VC and identifies and explains how current model, platform, and hardware limitations contribute to these issues, and proposes countermeasures to address them, informing research and practice towards more sustainable VC approaches.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
General Science
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