@RealYDT: https://x.com/RealYDT/status/2065641164016120201
Summary
Microsoft has launched the MarkItDown tool, which converts various document formats (such as PDF, Word, Excel) into Markdown, making it easier for large models and RAG systems to process enterprise data.
View Cached Full Text
Cached at: 06/13/26, 03:01 PM
Deep Dive into Microsoft MarkItDown: Why “Everything as Markdown” Matters in the Age of LLMs?
In the midst of the booming era of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), one of the biggest pain points for developers is often not the algorithms or models themselves, but how to elegantly handle the “messy data” of enterprises.
PDF papers, complexly formatted Word reports, multi-sheet Excel files…
Similar Articles
CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG
CoinRAG is a new method for long-context RAG that reuses fine-grained contextualized information nugget KV caches instead of full chunks, improving efficiency and answering quality. It achieves a new Pareto frontier with 5.3% relative F1 improvement on LongBench multi-hop QA tasks.
Geo-Spatial Concept Probing of Large Language Models: Abstraction, Compositionality, and Grounding
This paper introduces a concept-centric benchmark to probe LLMs' understanding of geo-spatial concepts like direction, distance, and topology, testing abstraction, compositionality, and grounding across various model architectures and scales. Findings reveal clear limitations in current LLMs' conceptual understanding.
Zero Gap Is Not Restoration: Stratified Per-Question Probability Evaluation and Step-wise Mitigation of Benchmark Contamination
This paper critiques existing benchmark contamination mitigation metrics and proposes SA-PPG (Stratified Aggregate of Per-question Probability Gaps) for more reliable evaluation, alongside RailCap, a decoding-time mitigation method that caps greedy fallback tokens to suppress memorization.
Grammar Engineering Meets LLMs: Development of Cantonese and Irish ParGram Treebanks
This paper presents the development of Cantonese and Irish treebanks within the ParGram Project and investigates the potential and limitations of using multilingual LLMs (OpenAI's gpt-oss-120b) for grammar engineering tasks such as translation and syntactic structure generation.
An Agentic Hybrid Top-Down and Bottom-Up Approach to Knowledge Graph Generation
This paper proposes a hybrid knowledge graph generation pipeline that combines top-down grounding in Wikidata with bottom-up agentic synthesis to handle noisy, multilingual HR skill declarations, producing a scalable and self-healing skills taxonomy.