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.
Simon Willison
Sep 22
llm 0.36
The release of llm 0.36 introduces new OpenAI models gpt-6-sol and gpt-6-luna, and adds support for model plugins to declare that they do not support conversations via supports_conversation = False. When such models receive assistant or tool history, llm raises a ConversationNotSupported error and the chat interface rejects them before starting a session. Additional changes include wrapping reasoning traces in Markdown output with <details> tags and bug fixes from five contributors.
Simon Willison
Sep 7
llm 0.35
The article announces the release of llm version 0.35, which introduces a new OpenAI model named gpt-6-astra for GPT-6 Astra. It highlights the addition of this model to the llm library and tags the release with openai, llm, and gpt-6-astra.
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.
Towards Data Science
Aug 20
How to Fine-Tune an LLM: An End-to-End Guide
The article "How to Fine-Tune an LLM: An End-to-End Guide" offers a practical, hands‑on walkthrough for fine‑tuning large language models in real‑world scenarios. It covers the entire process from data preparation to deployment, providing readers with actionable steps to adapt LLMs to specific tasks. The guide is aimed at practitioners looking to implement fine‑tuning in a structured, end‑to‑end manner.
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 .
arXiv Computation and Language
Sep 3
REAP: Relation-Aware Elicitation and Parsing for Closed-Book Knowledge Base Construction from LLMs
The REAP system tackles the AKBC Shared Task 2026, aiming to build knowledge bases from language models without fine‑tuning and within a 32‑B parameter budget. It uses structured chain‑of‑thought reasoning, relation‑specific queries, and a reasoning‑based empty‑set gate to elicit knowledge, then directly extracts it into valid JSON arrays. Evaluated on the test set with the Mistral‑Small‑24B‑Instruct‑2501 model, REAP achieves a macro‑F1 of 0.62, notably high scores on countryLandBordersCountry (0.95), companyTradesAtStockExchange (0.73), and hasArea (0.77).
Simon Willison
Aug 26
Quoting Paul Dix
The article highlights Paul Dix’s astonishment that AI was able to generate one million lines of code and then refine it over several months into a reliable software product now used by millions of developers. Dix argues that this achievement is far more impressive than merely translating code between languages, emphasizing that with a verification system and clear guidance, AI can produce and iteratively improve highly complex, sophisticated software until it functions perfectly.
Towards Data Science
5d ago
How to Make Your Own JEV Model from an Open LLM
The article explains how to transform a small open‑source Qwen LLM into a fast, single‑pass text classifier by replacing its language‑modeling head with a JEV model. It provides a step‑by‑step guide to swapping the head, enabling the LLM to perform classification tasks efficiently. The process leverages the flexibility of open‑source models to create a lightweight, high‑performance classifier.