Towards Data Science
Jun 17
You Probably Don’t Need an Agent Framework
Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python.
Increase Recommendation Systems’ Precision with LLMs, Using Python
This is how LLMs are used today to increase precision in recommendation systems The post Increase Recommendation Systems’ Precision with LLMs, Using Python appeared first on Towards Data Science .
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Towards Data Science
Aug 9
How to Implement Structured Output with Local LLMs
Why use it? How to implement it?
Towards Data Science
Jun 5
Automate Writing Your LLM Prompts
Using DSPy to automatically create, evaluate, and optimize your prompts The post Automate Writing Your LLM Prompts appeared first on Towards Data Science .
Towards Data Science
Jun 18
Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each
Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each appeared first on Towards Data Science .
Towards Data Science
Jun 15
How to Effectively Align with Claude Code
Increase productivity with your LLMs The post How to Effectively Align with Claude Code appeared first on Towards Data Science .
Hugging Face Trending Papers
Jul 22
Personalized Recommendation Tool Learning via Autonomous Language Agents
Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks. To circumvent these limitations through architectural design rather than modifying the LLM itself, we propose an agent-based recommendation framework, memory-based $\textbf{P}$ersonalized $\textbf{R}$ecommendation $\textbf{T}$ool learning via autonomous language $\textbf{A}$gents (PRTA), in which an LLM acts as a central planner interacting with multiple recommendation models as tools.
Towards Data Science
Jun 27
How to Build a Powerful LLM Knowledge Base
Use coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science .
arXiv AI
Jul 23
Personalized Recommendation Tool Learning via Autonomous Language Agents
arXiv:2607. 19739v1 Announce Type: cross Abstract: Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks.
Towards Data Science
Jul 3
LLM Wikis Are Over-Engineered — I Replaced Mine With a Pure Python Compiler
Most "LLM wikis" use agents, embeddings, and repeated model calls to organize local notes. I built a deterministic alternative: a pure Python compiler that turns messy markdown into a linked, linted wiki using only the standard library.
Towards Data Science
Jul 26
How to Efficiently Prompt Claude Code
Maximize your efficiency with Claude Code The post How to Efficiently Prompt Claude Code appeared first on Towards Data Science .
Towards Data Science
Jun 7
Building a Multi-Agent System in Python
An introduction to multi-agent systems The post Building a Multi-Agent System in Python appeared first on Towards Data Science .
Google AI Blog
Feb 21, 2024
Advances in private training for production on-device language models
Posted by Zheng Xu, Research Scientist, and Yanxiang Zhang, Software Engineer, Google Language models (LMs) trained to predict the next word given input text are the key technology for many applications [ 1 , 2 ]. In Gboard , LMs are used to improve users’ typing experience by supporting features like next word prediction (NWP), Smart Compose , smart completion and suggestion , slide to type , and proofread .