Ajay Sai K.’s Post

### Fine-Tuning vs. Retrieval-Augmented Generation (RAG) for Large Language Models In the realm of artificial intelligence, especially natural language processing, organizations increasingly utilize Large Language Models (LLMs). Two prominent strategies are Fine-Tuning and Retrieval-Augmented Generation (RAG), each offering unique benefits. **Fine-Tuning** involves further training a pre-trained LLM on a specialized dataset. This process enhances the model’s understanding of specific domains, improving performance on tasks like sentiment analysis or summarization. The advantages include higher accuracy, control over outputs, and reduced latency, making it ideal for applications requiring deep domain knowledge. On the other hand, **RAG** combines a language model with an external knowledge base. By retrieving relevant information dynamically, RAG ensures access to up-to-date data, reducing the risk of generating incorrect information. This approach is versatile and scalable, making it suitable for applications needing real-time information. Choosing between the two depends on the application. Fine-tuning is optimal for domain-specific tasks, while RAG excels in scenarios requiring current knowledge and broader applicability. Understanding these strategies allows organizations to effectively harness LLMs for their unique needs, driving innovation and efficiency in their operations.

𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿-𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 (𝗣𝗘𝗙𝗧) PEFT is an optimization technique for fine-tuning large language models (LLMs) by only modifying a small subset of parameters, instead of adjusting the entire model. This significantly reduces computational resource requirements while maintaining high performance. Popular PEFT techniques include 𝗟𝗼𝗥𝗔 (𝗟𝗼𝘄-𝗥𝗮𝗻𝗸 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻), 𝗣𝗿𝗲𝗳𝗶𝘅 𝗧𝘂𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗣-𝗧𝘂𝗻𝗶𝗻𝗴. Common PEFT techniques: 1 .𝗟𝗼𝗥𝗔 (𝗟𝗼𝘄-𝗥𝗮𝗻𝗸 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻): LoRA works by "freezing" most of the original model’s parameters and only training a pair of low-rank matrices. This reduces the number of trainable parameters from tens of thousands to just a few thousand, saving resources while maintaining good performance on specific tasks like text classification and language translation. 𝟮. 𝗣𝗿𝗲𝗳𝗶𝘅 𝗧𝘂𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣-𝗧𝘂𝗻𝗶𝗻𝗴: These techniques add parameters into different layers of the model or within embeddings. They are optimized for tasks such as Natural Language Understanding (NLU) without the need to fine-tune the entire neural network. https://lnkd.in/gTM7r_tf #PEFT #FineTuning #LLMs

📃Scientific paper: LM4OPT: Unveiling the Potential of Large Language Models in Formulating Mathematical Optimization Problems Abstract: In the rapidly evolving field of natural language processing, the translation of linguistic descriptions into mathematical formulation of optimization problems presents a formidable challenge, demanding intricate understanding and processing capabilities from Large Language Models (LLMs). This study compares prominent LLMs, including GPT-3.5, GPT-4, and Llama-2-7b, in zero-shot and one-shot settings for this task. Our findings show GPT-4's superior performance, particularly in the one-shot scenario. A central part of this research is the introduction of `LM4OPT,' a progressive fine-tuning framework for Llama-2-7b that utilizes noisy embeddings and specialized datasets. However, this research highlights a notable gap in the contextual understanding capabilities of smaller models such as Llama-2-7b compared to larger counterparts, especially in processing lengthy and complex input contexts. Our empirical investigation, utilizing the NL4Opt dataset, unveils that GPT-4 surpasses the baseline performance established by previous research, achieving an F1-score of 0.63, solely based on the problem description in natural language, and without relying on any additional named entity information. GPT-3.5 follows closely, both outperforming the fine-tuned Llama-2-7b. These findings not only benchmark the current capabilities of LLMs in a novel application area but also lay the groundwork for future improvements in mathematical formulation of optimization problems from natural la... Continued on ES/IODE ➡️ https://etcse.fr/Vanf ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

Using Sentence Embeddings and Semantic Similarity for Seeking Consensus when Assessing Trustworthy AI Dennis Vetter, Jesmin Jahan Tithi, Ph.D , Magnus Westerlund, Roberto V. Zicari, Gemma Roig Assessing the trustworthiness of artificial intelligence systems requires knowledge from many different disciplines. These disciplines do not necessarily share concepts between them and might use words with different meanings, or even use the same words differently. Additionally, experts from different disciplines might not be aware of specialized terms readily used in other disciplines. Therefore, a core challenge of the assessment process is to identify when experts from different disciplines talk about the same problem but use different terminologies. In other words, the problem is to group problem descriptions (a.k.a. issues) with the same semantic meaning but described using slightly different terminologies.  In this work, we show how we employed recent advances in natural language processing, namely sentence embeddings and semantic textual similarity, to support this identification process and to bridge communication gaps in interdisciplinary teams of experts assessing the trustworthiness of an artificial  https://lnkd.in/ebdrgb-b

The Evolving Landscape of Retrieval-Augmented Generation (RAG) for Natural Language Processing Retrieval-augmented generation (RAG) has emerged as a powerful technique for enhancing the capabilities of large language models (LLMs) in answering complex queries. At its core, RAG involves the strategic retrieval of relevant information from a corpus to supplement the LLM’s knowledge, leading to more grounded and informative responses. https://lnkd.in/eD5w8czi

I'm excited to share my latest article on optimizing inference speed for large language models! 🚀 In this guide, I explore a variety of techniques (with examples) to enhance the performance of LLMs in real-time applications, including: - Model compression techniques - Efficient attention mechanisms - Parallelization strategies - Advanced decoding methods https://lnkd.in/gqcfyery #artificialintelligence #ai #llms #datascience #deeplearning #machinelearning #nlp

🚀 Our latest research has been published in the INFOR: Information Systems and Operational Research Journal by Taylor & Francis Online! 🎉 This work introduces the LM4OPT framework, designed to fine-tune compact (~7B) LLMs for linear programming problem formulation from natural language description. Our methodology consists of a two-phase process: first, we adapt the models to a broader domain using the GSM8K dataset, followed by task-specific fine-tuning on the NL4Opt dataset. We also optimize zero-shot and one-shot performance through prompt engineering. To make fine-tuning more efficient, we utilize Low-Rank Adaptations (LoRA) and Parameter-Efficient Fine-Tuning (PEFT) techniques while integrating Noisy Embedding Instruction Fine Tuning (NEFTune) to avoid overfitting. Through LM4OPT, we narrow the performance gap between smaller and larger models, with results showing GPT-4's superiority in this task. Thanks to my supervisor, Dr. Salimur Choudhury, for his guidance throughout this research! Check out the full paper here: https://lnkd.in/e2rAqDqb #LLM #Optimization #NLP #OperationsResearch #AI #MachineLearning

Meta's new paper "Better & Faster Large Language Models via Multi-token Prediction" introduces a novel method for training large language models (LLMs) by predicting multiple tokens simultaneously rather than just the next one. This "multi-token prediction" not only boosts the efficiency of models but also their performance on complex tasks like coding. This new approach is changing things in a big way. For coding tasks, it's getting results 12-17% more effectively than older methods. It's also faster, with operations taking up to three times less time. The bigger the model, the bigger the improvements. This is especially true when it comes to AI applications for coding and natural language processing. So, what does it mean for AI technology's future, and how does it really work? Find the answers in the link below. Read more about it here: https://lnkd.in/gXJriUEb It's now your chance to learn about this next leap in AI. Are you prepared?

Deploying the LLM GGML model locally with Docker is a convenient and effective way to use natural language processing. Dockerizing the model makes it easy to move it between different environments and ensures that it will run consistently. Testing the model in a browser provides a user-friendly interface and allows you to quickly evaluate its performance. This setup gives you more control over your infrastructure and data and makes it easier to deploy advanced language models for a variety of applications. It is a significant step forward in the deployment of large language models.

Deploying the LLM GGML model locally with Docker is a convenient and effective way to use natural language processing. Dockerizing the model makes it easy to move it between different environments and ensures that it will run consistently. Testing the model in a browser provides a user-friendly interface and allows you to quickly evaluate its performance. This setup gives you more control over your infrastructure and data and makes it easier to deploy advanced language models for a variety of applications. It is a significant step forward in the deployment of large language models.