Tag: llms
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The Strangest Bottleneck in Modern LLMs
The Strangest Bottleneck in Modern LLMs Why insanely fast GPUs still can’t make LLMs feel instant The post The Strangest Bottleneck in Modern LLMs appeared first on Towards Data Science. Moulik Gupta Go to original source
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Using Local LLMs to Discover High-Performance Algorithms
Using Local LLMs to Discover High-Performance Algorithms How I used open-source models to explore new frontiers in efficient code generation, using my MacBook and local LLMs. The post Using Local LLMs to Discover High-Performance Algorithms appeared first on Towards Data Science. Stefano Bosisio Go to original source
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Why LLMs Aren’t a One-Size-Fits-All Solution for Enterprises
Why LLMs Aren’t a One-Size-Fits-All Solution for Enterprises LLMs are a seamless way to find value in your unstructured data, but the truth is, there is so much more value hidden within your structured data. This post explores what LLMs are (and aren’t) optimized for and how the industry is approaching AI over structured business…
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LLMs Are Randomized Algorithms
LLMs Are Randomized Algorithms A surprising connection between the newest AI models and a 50-year old academic field The post LLMs Are Randomized Algorithms appeared first on Towards Data Science. Udayan Kanade Go to original source
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LLMs vs DSLMs — has anyone shown significant improvements when applying this in companies?
LLMs vs DSLMs — has anyone shown significant improvements when applying this in companies? I’ve been hearing a lot about DSLMs. We’ve stuck with the larger LLMs like GPT. Has anyone seen significant improvements with the DSLMs instead? https://devnavigator.com/2025/11/07/the-lifecycle-of-a-domain-specific-language-model/ submitted by /u/WarChampion90 [link] [comments] /u/WarChampion90 Go to original source
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Are LLMs necessary to get a job?
Are LLMs necessary to get a job? For someone laid off in 2023 before the LLM/Agent craze went mainstream, do you think I need to learn LLM architecture? Are certs or github projects worth anything as far as getting through the filters and/or landing a job? I have 10 YOE. I specialized in machine learning…
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How to Analyze and Optimize Your LLMs in 3 Steps
How to Analyze and Optimize Your LLMs in 3 Steps Learn to enhance your LLMs with my 3 step process, inspecting, improving and iterating on your LLMs The post How to Analyze and Optimize Your LLMs in 3 Steps appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Should We Use LLMs As If They Were Swiss Knives?
Should We Use LLMs As If They Were Swiss Knives? A logic game performance comparison between popular LLMs and a custom-made algorithm The post Should We Use LLMs As If They Were Swiss Knives? appeared first on Towards Data Science. Nicolas Garcia Aramouni Go to original source
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How to Use LLMs for Powerful Automatic Evaluations
How to Use LLMs for Powerful Automatic Evaluations A beginner-friendly introduction to LLM-as-a-Judge The post How to Use LLMs for Powerful Automatic Evaluations appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How to Benchmark LLMs – ARC AGI 3
How to Benchmark LLMs – ARC AGI 3 Learn how to LLMs are benchmarked, and try out the newly released ARC AGI 3 The post How to Benchmark LLMs – ARC AGI 3 appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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LLMs and Mental Health
LLMs and Mental Health Are LLMs good or bad for our mental health? It’s more complicated than that. The post LLMs and Mental Health appeared first on Towards Data Science. Stephanie Kirmer Go to original source
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Can LLMs Reason – I don’t know, depends on the definition of reasoning. Denny Zhou – Founder/Lead of Google Deepmind LLM Reasoning Team
Can LLMs Reason – I don’t know, depends on the definition of reasoning. Denny Zhou – Founder/Lead of Google Deepmind LLM Reasoning Team AI influencers: LLMs can think given this godly prompt bene gesserit oracle of the world blahblah, hence xxx/yyy/zzz is dead. See more below. Meanwhile, literally the founder/lead of the reasoning team: https://preview.redd.it/z9uwnummqeff1.png?width=652&format=png&auto=webp&s=c84727d328d059504adf64768b8badac45d20611…
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How To Significantly Enhance LLMs by Leveraging Context Engineering
How To Significantly Enhance LLMs by Leveraging Context Engineering The benefits and practical aspects of context engineering for LLMs The post How To Significantly Enhance LLMs by Leveraging Context Engineering appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes
How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes When numbers lie — and your metrics mislead you The post How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes appeared first on Towards Data Science. Subha Ganapathi Go to original source
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Are You Being Unfair to LLMs?
Are You Being Unfair to LLMs? They may deserve better. The post Are You Being Unfair to LLMs? appeared first on Towards Data Science. Julian Mendel Go to original source
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LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries
LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries Local Large Language Models can convert massive DataFrames to presentable Markdown reports — here’s how. The post LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries appeared first on Towards Data Science. Dario Radečić Go to original source
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Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning
Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning It’s like grading papers, but your student is an LLM The post Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning appeared first on Towards Data Science. Stephanie Kirmer Go to original source
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Highly Efficient and Effective LLMs with Multi-Boolean Architectures
Highly Efficient and Effective LLMs with Multi-Boolean Architectures arXiv:2505.22811v1 Announce Type: new Abstract: Weight binarization has emerged as a promising strategy to drastically reduce the complexity of large language models (LLMs). It is mainly classified into two approaches: post-training binarization and finetuning with training-aware binarization methods. The first approach, while having low complexity, leads to…
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New to LLMs? Start Here
New to LLMs? Start Here A guide to Agents, LLMs, RAG, Fine-tuning, LangChain with practical examples to start building The post New to LLMs? Start Here appeared first on Towards Data Science. ALESSANDRA COSTA Go to original source
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Retrieval Augmented Classification: Improving Text Classification with External Knowledge
Retrieval Augmented Classification: Improving Text Classification with External Knowledge Text Classification stands as one of the most basic yet most important applications of natural language processing. It has a vital role in many real-world applications that go from filtering unwanted emails like spam, detecting product categories or classifying user intent in a chat-bot application. The…
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Build and Query Knowledge Graphs with LLMs
Build and Query Knowledge Graphs with LLMs Knowledge Graphs are relevant A Knowledge Graph could be defined as a structured representation of information that connects concepts, entities, and their relationships in a way that mimics human understanding. It is often used to organise and integrate data from various sources, enabling machines to reason, infer, and retrieve relevant…
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Circuit Tracing: A Step Closer to Understanding Large Language Models
Circuit Tracing: A Step Closer to Understanding Large Language Models Context Over the years, Transformer-based large language models (LLMs) have made substantial progress across a wide range of tasks evolving from simple information retrieval systems to sophisticated agents capable of coding, writing, conducting research, and much more. But despite their capabilities, these models are still largely…
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Talk to Videos
Talk to Videos Large language models (LLMs) are improving in efficiency and are now able to understand different data formats, offering possibilities for myriads of applications in different domains. Initially, LLMs were inherently able to process only text. The image understanding feature was integrated by coupling an LLM with another image encoding model. However, gpt-4o…
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An Overview of Large Language Models for Statisticians
An Overview of Large Language Models for Statisticians arXiv:2502.17814v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI), exhibiting remarkable capabilities across diverse tasks such as text generation, reasoning, and decision-making. While their success has primarily been driven by advances in computational power and deep learning architectures,…
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How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference
How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference With the recent explosion of interest in large language models (LLMs), they often seem almost magical. But let’s demystify them. I wanted to step back and unpack the fundamentals — breaking down how LLMs are built, trained, and fine-tuned to become the AI systems we interact…
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2-Bit VPTQ: 6.5x Smaller LLMs While Preserving 95% Accuracy
2-Bit VPTQ: 6.5x Smaller LLMs While Preserving 95% Accuracy Very accurate 2-bit quantization for running 70B LLMs on a 24 GB GPU Continue reading on Towards Data Science » Benjamin Marie Go to original source
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Low-Rank Correction for Quantized LLMs
Low-Rank Correction for Quantized LLMs arXiv:2412.07902v1 Announce Type: new Abstract: We consider the problem of model compression for Large Language Models (LLMs) at post-training time, where the task is to compress a well-trained model using only a small set of calibration input data. In this work, we introduce a new low-rank approach to correct for…
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Is your org treating the rollout of LLMs as an IT or data science problem?
Is your org treating the rollout of LLMs as an IT or data science problem? Our org has given all resource (and limited all API access) to LLMs to a dedicated team in the IT department, which has no prior data experience. So far no data scientist has been engaged for feedback on design or…
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Smaller is smarter
Smaller is smarter Concerns about the environmental impacts of Large Language Models (LLMs) are growing. Although detailed information about the actual costs of LLMs can be difficult to find, let’s attempt to gather some facts to understand the scale. Generated with ChatGPT-4o Since comprehensive data on ChatGPT-4 is not readily available, we can consider Llama 3.1…