# Jul 4, 2024

- **Beating NumPy matrix multiplication in 150 lines of C**  
  A custom C implementation of matrix multiplication outperforms NumPy's performance by achieving over **1 TFLOPS** on an AMD Ryzen 7700 CPU, utilizing a design inspired by the [BLIS](https://en.wikipedia.org/wiki/BLIS_(software)) framework and optimized through parallelization with OpenMP.

- **AI's $600B Question**  
  **AI's revenue gap has widened** from $200B to $600B, reflecting a significant discrepancy between the revenue expectations from AI infrastructure investments and actual growth in the ecosystem.

- **Diffusion Forcing: Next-Token Prediction Meets Full-Sequence Diffusion**  
  **Diffusion Forcing** introduces a **novel training paradigm** where a diffusion model denoises tokens with varied noise levels, merging next-token prediction's variable-length generation with full-sequence diffusion's trajectory guidance.

- **[P] New collection of Llama, Mistral, Phi, Qwen, and Gemma models for function/tool calling**  
  **Rubra v0.1** introduces a **collection of open-weight, tool-calling large language models (LLMs)** including Llama, Mistral, Phi, Qwen, and Gemma, aiming to bridge the gap in function/tool calling capabilities between proprietary and open-source models. [Try it out here](https://huggingface.co/spaces/sanjay920/rubra-v0.1-function-calling)

- **[R] Taxonomy for Data Transformations in AI Systems**  
  The **Data Transformation Taxonomy** introduced in the SIGMOD'24 paper categorizes **data transformations in AI systems** into reusable across models, model-specific, and real-time request-specific. [Read the SIGMOD paper](https://dl.acm.org/doi/10.1145/3626246.3653389)

- **Likelihood computation in diffusion models [P]**  
  **Diffusion models**, specifically the **SDE approach** detailed in [Song et al.](http://arxiv.org/abs/2011.13456) and [Song et al.](http://arxiv.org/abs/2101.09258), require the **probability flow ODE** for exact likelihood computation of generated data, due to the intractability of SDE likelihoods.

- **TwoMinutePapers - ChatGPT Just Learned To Fix Itself!**  
  **OpenAI's new paper** introduces a groundbreaking concept where **AI systems critique and correct errors** in other AI-generated outputs, enhancing code quality and security.

- **MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context**  
  The **Medical Visual Hallucination Test (MedVH)** introduces a **benchmark dataset** to assess hallucinations in domain-specific Large Vision Language Models (LVLMs) within the medical context, addressing a gap in evaluating these models' robustness.

- **Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning**  
  The **approach dynamically retrieves database information** and utilizes **abstract syntax trees** for selecting few-shot examples, aiming to improve **Text-to-SQL semantic parsing** with Large Language Models.

- **Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing**  
  The proposed **transformer architecture** focuses on **multiple-resolution tokenization** for time series forecasting, enhancing representation learning across various scales in the pricing domain.

- **Let the Code LLM Edit Itself When You Edit the Code**  
  The **Positional Integrity Encoding (PIE)** method significantly **reduces computational overhead** in real-time code editing by maintaining accurate positional relationships between tokens without the need for full re-encoding.

- **SemioLLM: Assessing Large Language Models for Semiological Analysis in Epilepsy Research**  
  **SemioLLM** evaluates **state-of-the-art Large Language Models (LLMs)** like GPT-3.5, GPT-4, Mixtral 8x7B, and Qwen-72chat for their efficacy in **diagnosing epilepsy** by analyzing unstructured text descriptions of seizures.

- **LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Models**  
  **LoRA-Guard** introduces a **parameter-efficient guardrail adaptation** method for content moderation on **resource-constrained devices**, leveraging **knowledge sharing** between LLMs and guardrail models.

- **Large Language Models as Evaluators for Scientific Synthesis**  
  **Large Language Models (LLMs) like GPT-4 and Mistral** are being tested for their ability to **evaluate the quality of scientific syntheses**, comparing their assessments to human annotators using a dataset of 100 research questions.

- **GPTQT: Quantize Large Language Models Twice to Push the Efficiency**  
  **GPTQT introduces a novel post-training quantization method** for Large Language Models (LLMs), significantly **reducing memory usage and enhancing processing speed** by quantizing model weights to **3bit/2bit**.