Sulbha Jain’s Post

UCLA team created Hierarchical Memory Transformers (HMT), designed to enhance long-context language processing in large language models (LLMs). Traditional transformers face limitations due to fixed context windows, which restrict their ability to handle extensive input sequences. HMT addresses this challenge by mimicking the hierarchical structure of human memory, thereby improving the model's capacity to memorize and recall information over longer contexts.   HMT employs a memory-augmented segment-level recurrence mechanism that organizes memory into sensory, short-term, and long-term categories. This organization facilitates better information selection and filtering, leading to improved performance in tasks requiring long-context understanding. The framework can be integrated into existing transformer architectures with minimal increases in parameters, ranging from 0.5% to 2%, which enhances its adaptability for future LLMs.   In various evaluations, HMT has demonstrated notable improvements compared to existing models. For instance, it achieved a 25.5% and 17.6% increase in effectiveness on the Wikitext-103 dataset when applied to the OPT and OpenLlamaV2 models, respectively. Additionally, it improved long-answer contextual reasoning by 9.81% and short-answer prediction accuracy by 1.0% in question-answering tasks using the PubMedQA dataset.   The memory recall mechanism of HMT allows for the dynamic extraction of relevant information from previous segments, which is essential for applications that require context switching. Its performance has been rigorously evaluated against benchmarks such as Wikitext-103 and PG-19, where it outperformed the Recurrent Memory Transformer (RMT) by 13% and 5.42%, respectively. Moreover, HMT demonstrates efficient memory usage, requiring less VRAM than traditional models, making it suitable for environments with limited resources.   The implications of HMT extend to the development of lifelong AI assistants capable of adapting to user behavior over time.    Arxiv: https://lnkd.in/evFenexf

Researchers have introduced a novel approach called natural language embedded programs (NLEPs) to improve the numerical and symbolic reasoning capabilities of large language models (LLMs). #nlep #llm #ai #tech #artificialintelligence #news #technology

Best 5 Foundational Review Papers on Large Language Models Link: https://lnkd.in/dH49N9Fe Large language models (LLMs) have emerged as a transformative force in natural language processing, with applications spanning text generation, question answering, summarization, and many other areas. This article provides a curated review of five seminal papers that lay the groundwork for understanding LLMs and their core concepts. The papers covered offer comprehensive surveys analyzing the architectures, training procedures, capabilities, and limitations of state-of-the-art LLMs. Key topics include the self-attention mechanism enabling long-range context modeling, pretraining objectives like masked language modeling, and retrieval-augmented approaches leveraging external knowledge sources. The review also examines emerging trends, open challenges, and future research directions in this rapidly evolving field. By synthesizing these foundational works, this article provides researchers and practitioners with a thorough grounding in the principles underpinning LLMs and their real-world applications.

Multi-Scale Geometric Analysis of Language Model Features: From Atomic Patterns to Galaxy Structures Large Language Models (LLMs) have emerged as powerful tools in natural language processing, yet understanding their internal representations remains a significant challenge. Recent breakthroughs using sparse autoencoders have revealed interpretable “features” or concepts within the models’ activation space. While these discovered feature point clouds are now publicly accessible, comprehending their complex structural organization across different scales presents a crucial research problem. Read the full article: https://lnkd.in/ekuXxJNs Paper: https://lnkd.in/gn6Rk-ck

💥💥💥 Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges Minghao Shao, Abdul Basit, Ramesh Karri, Muhammad Shafique Abstract Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural networks, often encompassing dozens of neural network layers and containing billions to trillions of parameters. They are typically trained on vast datasets, utilizing architectures based on transformer blocks. Present-day LLMs are multi-functional, capable of performing a range of tasks from text generation and language translation to question answering, as well as code generation and analysis. An advanced subset of these models, known as Multimodal Large Language Models (MLLMs), extends LLM capabilities to process and interpret multiple data modalities, including images, audio, and video. This enhancement empowers MLLMs with capabilities like video editing, image comprehension, and captioning for visual content. This survey provides a comprehensive overview of the recent advancements in LLMs. We begin by tracing the evolution of LLMs and subsequently delve into the advent and nuances of MLLMs. We analyze emerging state-of-the-art MLLMs, exploring their technical features, strengths, and limitations. Additionally, we present a comparative analysis of these models and discuss their challenges, potential limitations, and prospects for future development. 👉 https://lnkd.in/giHg34hW #machinelearning

"Better & Faster Large Language Models via Multi-token Prediction" Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, Gabriel Synnaeve Meta unveils groundbreaking multi-token prediction LLM! ⚛ Researchers at Meta have introduced a novel approach to training large language models (LLMs) that promises higher sample efficiency and improved downstream capabilities. By training models to predict multiple future tokens simultaneously, using independent output heads, they achieve remarkable gains in performance without increasing training time. This method proves particularly advantageous for larger model sizes and yields significant improvements in generative benchmarks like coding, where their 13B parameter models outperform strong baselines by several percentage points on HumanEval and MBPP tasks. Despite the impressive achievements of LLMs in natural language processing, next-token prediction has remained an inefficient method for acquiring language and capturing long-term dependencies effectively. However. the new multi-token prediction approach presented in this study addresses this limitation by enabling models to predict several future tokens at each position in the training corpus simultaneously. Not only does this method drive better sample efficiency, but it also facilitates self-speculative decoding, making models up to 3 times faster at inference time across various batch sizes. This groundbreaking research offers a simple yet effective modification to train stronger and faster transformer models. By demonstrating the benefits of multi-token prediction, the study opens doors to exploring novel auxiliary losses for LLMs, aiming to further enhance their performance, coherence, and reasoning abilities. This work holds great promise for advancing the capabilities of AI language models and unlocking their potential across diverse applications. Paper: https://lnkd.in/gXPkmsDS #LLMs #MultiToken #Meta #ResearchPaper

🌟 Exploring AI: BERT vs GANs Hi everyone! I wanted to share my thoughts on two fascinating AI models that have been making waves in the tech world: BERT (Bidirectional Encoder Representations from Transformers) and GANs (Generative Adversarial Networks). 🤖 What is BERT? BERT is a game-changer in the field of natural language processing (NLP). Developed by Google, it helps machines understand the context of words in a sentence by reading text in both directions. This bidirectionality allows BERT to capture nuances in language that other models might miss. Key Applications: Sentiment Analysis: Understanding how people feel about a product or service. Question Answering: Providing accurate responses based on context. Text Classification: Sorting text into categories, which is invaluable for data organization. 🎨 What are GANs? GANs are incredibly innovative! They consist of two neural networks—the generator and the discriminator—that work against each other. The generator creates new data, while the discriminator evaluates it to determine if it’s real or fake. This adversarial training leads to impressive results, especially in image generation. Key Applications: Image Generation: Creating realistic images, even of people who don’t exist. Art Creation: Exploring creativity through AI-generated art. Data Augmentation: Enhancing datasets for training machine learning models. Share Your Ideas: How would you use either BERT or GANs in a project? Let’s learn from each other! I’m looking forward to your comments. 🌐💬 📚 Resources: BERT Papers: https://lnkd.in/g6-hUYPA GANs Explained: https://lnkd.in/gufi8CTC #AI #MachineLearning #NLP #DeepLearning #BERT #GANs #Innovation

👉 Researchers at MIT have found evidence that large language models (LLMs) may develop their own understanding of the world as their language abilities improve, rather than merely combining superficial statistics. 👉 The researchers trained a language model with synthetic programs to navigate 2D grid world environments and found that a probing classifier could extract increasingly accurate representations of hidden states from the LM's hidden states, suggesting an emergent ability of the LM to interpret programs. 👉 The findings are consistent with a separate experiment where a GPT model trained on Othello moves showed evidence of an internal "world model" of the game within the model's representations, offering a promising direction for understanding the capabilities and limitations of LLMs in capturing meaning. 👇 Read more #GenerativeAI

Cool paper that does GNN RAG! > we introduce GNN-RAG, a novel method for combining language understanding abilities of LLMs with the reasoning abilities of GNNs in a retrieval-augmented generation (RAG) style. First, a GNN reasons over a dense KG subgraph to retrieve answer candidates for a given question. Second, the shortest paths in the KG that connect question entities and answer candidates are extracted to represent KG reasoning paths. The extracted paths are verbalized and given as input for LLM reasoning with RAG. In our GNN-RAG framework, the GNN acts as a dense subgraph reasoner to extract useful graph information, while the LLM leverages its natural language processing ability for ultimate KGQA.

I am proud to share the publication of our latest research paper titled "From pre-training to fine-tuning: An in-depth analysis of Large Language Models in the biomedical domain", in #ArtificialIntelligenceinMedicine! In this study, we examine how Transformer-based Large Language Models (LLMs) adapt to the biomedical domain, addressing the unique challenges of this specialized and complex field. Our focus is on two key tasks: Natural Language Inference (NLI) and Named Entity Recognition (NER). By analyzing the encoding and attention mechanisms within LLMs, we compare general-purpose models with those tailored for biomedical applications. Fine-tuning these models across varying data volumes, we uncover compelling insights: the downstream performance of LLMs is closely linked to specific internal patterns, shaping how they process and apply domain-specific knowledge. I would sincerely thank all the co-authors who contributed to this work (Luca Bacco, Mario Merone and Felice Dell'Orletta), from Università Campus Bio-Medico di Roma and Cnr-Istituto di Linguistica Computazionale “Antonio Zampolli”. Feel free to check out the full paper here: https://lnkd.in/dNJA_j8j #AI #BiomedicalAI #LLMs #NLP #BioNLP