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DeepSeek har lanserat sin senaste AI-modell DeepSeek V3.2 som introducerar flera betydande förbättringar jämfört med tidigare versioner. Modellen bygger vidare på den experimentella version som släpptes i september och påstås prestera på samma nivå som OpenAI:s GPT-5 i flera resonemangstest. En annan stor nyhet är ”thinking in tool-use”, V3.2 kan nu integrera analytiskt tänkande direkt i verktygsanvändning. […]
Snabbguide till nya DeepSeek-V3.2 Read More »
We are proud to introduce the {mall} package. With {mall}, you can use a local LLM to run NLP operations across a data frame. (sentiment, summarization, translation, etc). {mall} has been simultaneously released to CRAN and PyPi (as an extension to Polars).
Introducing mall for R…and Python Read More »
The mall 0.2.0 update for R and Python introduces support for external LLM providers like OpenAI and Gemini. This version also features parallel processing for R users, the ability to run NLP on string vectors in Python, and a brand new cheatsheet.
If you only look at automated scores, most LLMs seem great—until they write something subtly wrong, risky, or off-tone. That’s the gap between what static benchmarks measure and what your users actually need. In this guide, we show how to blend human judgment (HITL) with automation so your LLM benchmarking reflects truthfulness, safety, and domain
LLM Benchmarking, Reimagined: Put Human Judgment Back In Read More »
Virtual assistants are progressing beyond simple question-and-answer formats to solving complex queries. Today, AI-driven virtual assistants communicate in multiple languages easily, and large language models, or LLMs, power this transformation. Now you can ask your device for restaurant recommendations in English and get an answer in Spanish. That’s what LLMs have made possible in recent
Role of Large Language Models in Powering Multilingual AI Virtual Assistants Read More »
Imagine hiring a new employee. One candidate is a “jack of all trades”—knows a little bit about everything, but not in depth. The other has 10 years of experience in your exact industry. Who do you trust with your critical business decisions? That’s the difference between general-purpose large language models (LLMs) and domain-specific LLMs. While
Building Domain-Specific LLMs: Precision AI for Every Industry Read More »
When most people think of large language models (LLMs), they imagine chatbots that answer questions or write text instantly. But beneath the surface lies a deeper challenge: reasoning. Can these models truly “think,” or are they simply parroting patterns from vast amounts of data? Understanding this distinction is critical — for businesses building AI solutions,
Understanding Reasoning in Large Language Models Read More »
Language is complex—and so are the technologies we built to understand it. At the intersection of AI buzzwords, you’ll often see NLP and LLMs mentioned as if they’re the same thing. In reality, NLP is the umbrella methodology, while LLMs are one powerful tool under that umbrella. Let’s break it down human-style, with analogies, quotes,
NLP vs LLM: Differences Between Two Related Concepts Read More »
In today’s AI-driven world, buzzwords like AI, Machine Learning (ML), Large Language Models (LLMs), and Generative AI are everywhere—but often misunderstood. They’re used interchangeably, though each has a distinct role and impact. In this blog, we won’t just define them in silos. Instead, we’ll pit them against each other, clarify how they’re related, how they
AI vs ML vs LLM vs Generative AI: What’s the Difference and Why It Matters Read More »
Large language models like GPT-4 and Claude have revolutionized AI adoption, but general-purpose models often fall short when it comes to domain-specific tasks. They’re powerful, but not tailored for specialized use cases involving proprietary data, complex industry terminology, or business-specific workflows. Fine-tuning large language models (LLMs) solves this problem by adapting pre-trained models for specific
What is Fine-Tuning for Large Language Models? Applications, Methods, and Future Trends Read More »
