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The world of artificial intelligence has been revolutionized by the advent of Large Language Models (LLMs). These models, such as GPT-4 and its successors, are more than just advanced text generators; they are sophisticated information-theoretic data compression engines. A recent analysis on LLMs delves deep into their technical underpinnings and explores how they harness mathematical principles from information theory to compress vast volumes of textual data into concise, coherent, and contextually relevant responses. This explains their extraordinary capabilities in natural language understanding and generation, making them versatile tools for language tasks, chatbots, content generation, and translation services. It's worth noting that these models also serve as data compression engines, albeit of a unique kind - information-theoretic data compressors. To learn more about the fascinating world of LLMs, check out this insightful article. #ArtificialIntelligence #NaturalLanguageProcessing #DataCompression #InformationTheory

The greatest detective in fiction, Sherlock Holmes, believed that memory is limited. Accordingly, he limited his knowledge of facts only to those he considered relevant. It's debatable whether this is how humans should approach learning. But there may be something to it when it comes to AI. Putting in selective forgetting can have it focus on meaning independent of language to then pick up additional languages more easily, or so a study shows. From the abstract: "Pretrained language models (PLMs) are today the primary model for natural language processing. Despite their impressive downstream performance, it can be difficult to apply PLMs to new languages, a barrier to making their capabilities universally accessible. While prior work has shown it possible to address this issue by learning a new embedding layer for the new language, doing so is both data and compute inefficient. We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Concretely, by resetting the embedding layer every K  updates during pretraining, we encourage the PLM to improve its ability of learning new embeddings within limited number of updates, similar to a meta-learning effect. Experiments with RoBERTa show that models pretrained with our forgetting mechanism not only demonstrate faster convergence during language adaptation, but also outperform standard ones in a low-data regime, particularly for languages that are distant from English.See link in comment."

Rho-1: A Smarter Language Model that Learns from the Most Important Words Traditional language models try to predict every single word in a text, but not all words are equally important. Some words, like "the" and "of," are very common and don't provide much information. Other words, like "algorithm" or "hypothesis," are more specific and convey more meaning. Rho-1 is a new language model that focuses on learning from the most important words. It uses a special technique to identify these important words and then trains itself to predict them more accurately. This approach has led to significant improvements in the performance of Rho-1. On a variety of language-related tasks, Rho-1 outperforms other language models that are much larger and more computationally expensive. For example, on a math dataset, Rho-1-1B (a relatively small model) achieved state-of-the-art results, matching the performance of a much larger model called DeepSeekMath. However, Rho-1-1B used only 3% of the training data that DeepSeekMath used. These results show that Rho-1 is a more efficient and effective way to train language models. It can achieve better performance with less data and less computational resources. This makes it a promising approach for a wide range of applications, such as natural language processing, machine translation, and question answering. Read more about the Ferret-UI in this research paper: https://lnkd.in/gRETJbrN

"AI and computer systems in general do not operate on human language but on numerical representations of data. Therefore, NLG involves transforming data that is being processed into human-readable text. Common use cases of NLG include automated report writing, chatbots, question-answering, and personalized content creation. To better comprehend how NLG works, it is essential to also understand its relationship with natural language understanding (NLU): NLG focuses on producing language, whereas NLU focuses on interpreting and understanding it."

Let's unlock the full potential of a powerful language model! Today, we're exploring Cohere's Command R+, tailored for demanding workloads and specific tasks. Command R+ is a Language Learning Model (LLM) that stands out with its 104 billion parameters. Remarkably, it operates on fewer tokens than similar models, making it a scalable choice for complex AI applications. What makes Command R+ so appealing is its ease of use. You can start using it with just three simple lines of code. First, install it with 'pip install mlx-lm', then import the necessary libraries from mlx_lm, and you're ready to go! This model isn't just powerful; it's also a polyglot, supporting ten languages including English, French, Spanish, and Chinese. This multilingual capability is perfect for businesses aiming to expand their AI operations globally. For those in research, Command R+ offers open weights to encourage innovation and progress in AI technology. When we compare Command R+, it shines brighter than many peers like claude-3 or gpt-4 turbo. In financial reasoning evaluations, this model scores an impressive 70.12%, showcasing its proficiency in specialized tasks. Now more accessible through Hugging Face Spaces and boasting a top 10 position on the Arena leaderboard, Command R+ proves itself as a robust tool for both researchers and enterprises alike. In summary, whether your focus lies in research or handling enterprise-level AI projects, Command R+ promises scalability and outstanding performance across various languages. Try out this model and step into the future of advanced language processing tools. #Cohere #LanguageModel #MachineLearning

Meta AI recently published in Nature an AI model capable of translating between 200 languages in any direction (https://lnkd.in/d7h44Wrt). This is the perfect time to invite you to our team presentation at #NAACL this week: "Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?" by Yotam Intrator and Matan Halfon demonstrates that direct inference can outperform pre-translation on various benchmarks and languages, particularly for low-resource ones. Check out the paper: https://lnkd.in/e-zrrwBM

“Large Language Models for Named Entity Extraction and Spelling Correction” As the field of AI/ML makes revolutionary strides, Best Path Research has just published a paper on arxiv which adds another contribution to the field: https://lnkd.in/ejwCHrHe While a large part of the advancements is in the user experience, the perceived “intelligence” of Large Language models (LLMs) is often due to simply using more training data and training larger models. However, we like to emphasize that just making things “bigger” is not the only way to make a model better. To advance the field and make systems which are more usable and interpretable, we need a combination of better algorithms, data and modularity. This is where Best Path Research has deep knowledge and experience. Full story at:  https://lnkd.in/etd9-NPk #bestpathresearch #nlp #ai #llm

MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models Large Language Models (LLMs) have demonstrated remarkable effectiveness across a diverse range of tasks. However, LLMs are usually distinguished by their massive parameter counts, which typically result in significant redundancy. One effective and practical approach to address this issue is semi-structured pruning, which introduces N:M sparsity into LLMs to improve both memory and computational efficiency. Recently, approaches such as SparseGPT and Wanda, utilize a small calibration set and carefully designed importance criteria to identify such redundant parameters. However , two substantial challenges remain: Firstly, the small calibration set is insufficient to represent the comprehensive knowledge embedded in LLMs and Secondly, using handcrafted criteria as a proxy for the true discrepancy inevitably results in errors. To address this researchers have introduced MaskLLM, a learnable pruning method that establishes Semi-structured (or “N:M”) Sparsity in LLMs, aimed at reducing computational overhead during inference. Instead of developing a new importance criterion, MaskLLM explicitly models N:M patterns as a learnable distribution through Gumbel Softmax sampling. This approach facilitates end-to-end training on large-scale datasets and offers two notable advantages: 1) High-quality Masks - effectively scales to large datasets and learns accurate masks; 2) Transferability - the probabilistic modeling of mask distribution enables the transfer learning of sparsity across domains or tasks. MaskLLM was assessed using 2:4 sparsity on various LLMs, including LLaMA-2, Nemotron-4, and GPT-3, with sizes ranging from 843M to 15B parameters, and our empirical results show substantial improvements over state-of-the-art methods. For instance, leading approaches achieve a perplexity (PPL) of 10 or greater on Wikitext compared to the dense model’s 5.12 PPL, but MaskLLM achieves a significantly lower 6.72 PPL solely by learning the masks with frozen weights. Furthermore, MaskLLM’s learnable nature allows customized masks for lossless application of 2:4 sparsity to downstream tasks or domains. Paper : https://lnkd.in/d_g8iFcr Checkout more paper review here https://lnkd.in/gaCZSrXm

In recent years, the rise of the concept of generative artificial intelligence has contributed to the dramatic popularization of language models. These language models are distinguished by their ability to understand and build complex language structures, often referred to as large language models (LLM). In particular, powerful language models such as GPT developed by OpenAI attract attention with their ability to not only understand texts but also create original texts. These models can perform various language tasks such as writing, translating, and text summarizing by grasping patterns in previous data sets. These developments reveal the fact that language models have a wide range of applications, from content production to language translation. In this article, we will take an overview of Large Language Models and examine their usage areas. Read my Medium Article -> https://lnkd.in/dh7pHwGF Thank you for support, Ozan Evkaya.