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 .
How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs
Google's Open Knowledge Format (OKF) is a Markdown+YAML skeleton for sharing knowledge between humans and AI agents. This post reuses that skeleton for a very specific job — an agent-to-agent hand-off of pre-tokenized integer arrays between three Qwen2.
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arXiv AI
Aug 3
ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction
arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.
arXiv AI
Jun 11
MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning
arXiv:2606. 12018v1 Announce Type: new Abstract: We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intelligence reasoning.
Hugging Face Trending Papers
Jun 10
MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning
We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intelligence reasoning. A key feature of our approach is that both the training and inference phases are augmented via knowledge distillation.
arXiv AI
Jun 8
MetaConfigurator: AI-Assisted RDF Authoring from JSON Data
arXiv:2606. 07094v1 Announce Type: cross Abstract: Scientific workflows increasingly generate structured JSON data that is easy to exchange but difficult to interpret consistently across systems due to lacking semantic interoperability.
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 .
Towards Data Science
Jul 14
Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs
How to stop parsing JSON by hand and start trusting your model's output The post Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs appeared first on Towards Data Science .
Towards Data Science
Jul 13
Agentic RAG: Let the Agent Search
A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop The post Agentic RAG: Let the Agent Search appeared first on Towards Data Science .
Towards Data Science
Aug 4
Using Agents as Tools
Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science .
OpenAI Blog
Aug 10, 2021
OpenAI Codex
We’ve created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today.
arXiv AI
Aug 11
AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups
arXiv:2608. 08883v1 Announce Type: new Abstract: Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows.
Google AI Blog
Mar 14, 2024
Cappy: Outperforming and boosting large multi-task language models with a small scorer
Posted by Yun Zhu and Lijuan Liu, Software Engineers, Google Research Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework. This paradigm is exemplified by recent multi-task LLMs, such as T0 , FLAN , and OPT-IML .