How Language Models Transform Information Discovery

What is Retrieval-Augmented Generation? RAG is a dual-pronged methodology that enhances language models by merging information retrieval with text generation. It leverages a pre-existing knowledge base—sourced from encyclopedias, databases, and more—to augment the content generation process. This fusion addresses concerns such as "AI hallucinations" and ensures data freshness, creating more accurate and contextually aware outputs. Practical Applications of RAG RAG shines in knowledge-intensive NLP tasks by integrating retrieval and generation mechanisms. This approach is particularly beneficial in domains requiring a deep understanding of complex information. For instance, a customer inquiring about the latest software features will receive the most recent and relevant information, fetched from dynamic sources like release notes or official documentation. Active Retrieval-Augmented Generation Active Retrieval-Augmented Generation goes a step further by actively retrieving and integrating up-to-date information during interactions. This enhances the model’s responsiveness in dynamic environments, making it ideal for applications that demand real-time accuracy. For example, in news summarization, RAG can provide timely and accurate updates by incorporating the latest developments. RAG vs. Fine-Tuning RAG's strength lies in blending pre-existing knowledge with creative generation, offering a nuanced and balanced approach. While fine-tuning focuses on refining a model’s performance on specific tasks, RAG’s combination of retrieval and generation proves advantageous for knowledge-intensive tasks, providing a sophisticated understanding of context. The Future of RAG Retrieval-Augmented Language Models (RALLM) encapsulate the essence of retrieval augmentation, seamlessly integrating contextual information retrieval with the generation process. RAG is not just a technological advancement; it represents a paradigm shift in how we approach AI and language models. Prominent Use Cases of RAG Customer Support: Companies like IBM use RAG to enhance customer-care chatbots, ensuring interactions are grounded in reliable and up-to-date information, providing personalized and accurate responses. Healthcare: RAG can assist medical professionals by retrieving the latest research and medical guidelines to support clinical decision-making and patient care. Legal Research: Lawyers can leverage RAG to quickly access and synthesize relevant case laws, statutes, and legal precedents, enhancing their ability to prepare cases and provide legal advice. Academic Research: Researchers can use RAG to gather and integrate the latest studies and data, streamlining literature reviews and enhancing the quality of academic papers.

Early in my days as an R&D data scientist, I was often asked to "mine our research data"—only to realize that most of it was buried in unstructured text, hidden in PDFs and PowerPoint slides. Today, large language models (LLMs) can help unlock that knowledge. They can now read full documents and extract key information—chemical entities, properties, synthesis steps, and more—faster and more accurately than traditional tools we've had before. The extracted knowledge, often too tangled to use directly, can be organized into a knowledge graph. Such a graph can capture and connect all the research knowledge from literature, reports, and even structured databases, forming a rich, evolving map of a scientific domain. What’s more, the graph can be fed back into LLMs as a structured, trusted source of context. Unlike basic “chat with your PDF” tools, this graph-enhanced setup allows the AI to reason across entities and relationships—enabling far more effective answers to complex scientific questions. It’s not just a time-saver; it’s a foundation for scalable, automated research. The University of Toronto’s MOF-ChemUnity project (Thomas Pruyn et al.) perfectly illustrated this approach: ~20,000 research papers processed, a knowledge graph built with 40,000 nodes and 3.2 million relationships, and an assistant capable of accurately answering questions about materials properties—among other applications. 📄 MOF-ChemUnity: Unifying metal-organic framework data using large language models, ChemRxiv, Jun 3, 2025 🔗 https://lnkd.in/gnVZeAU3 Swap MOFs for polymers, catalysts, or battery electrolytes, and the same playbook could serve many domains across R&D. What would it take to make this work in your field? Let’s connect and build something together.

From #Tokens to #Lattices: How #LargeLanguageModels (#LLMs) Build Conceptual Maps Without Our Help Today, we explore how #AI, without being told what a “bird” or a “disease” is, can build their own conceptual hierarchies — discovering patterns we might not even notice ourselves. All started in 1954 with the Distributional Hypothesis (Harris, 1954; Firth, 1959; Alessandro Lenci 2008) The Big Idea Language models like BERT are trained to predict missing words in a sentence. At first glance, this sounds like a game of fill-in-the-blank. But what emerges is much deeper. These models learn not just to guess words, but to understand them — by discovering the relationships between objects (like “eagle” or “malaria”) and their attributes (like “can fly” or “causes fever”). What results is a structure known in mathematics as a concept lattice — a kind of map where concepts live in a hierarchy, defined not by human dictionaries, but by how they co-occur in language. A Machine’s View of the World Let’s say you train a model on millions of sentences like: • “The eagle can fly.” • “The owl hunts at night.” • “The frog can swim.” The model doesn’t just store facts — it builds probabilities. It learns that certain objects and attributes often appear together. Over time, these probabilities form a three-dimensional space — a kind of digital perception of the world. Researchers then apply Formal Concept Analysis (FCA) — a branch of mathematics — to extract the underlying structure. What emerges is astonishing: • A lattice where animals that fly and hunt (like eagles and owls) belong to one group. • Animals that swim and hunt form another group. • And even concepts with no name, like “animals that both swim and fly,” are discovered — concepts we never taught the machine, but that it found on its own. Concepts Without Definitions Traditionally, we teach computers what a “raptor” or a “symptom” is by using pre-made dictionaries and taxonomies. But this learning approach lets the model define concepts from scratch — based only on patterns in how we speak and write. This leads to the discovery of what scientists call latent concepts — structures that make sense statistically and logically, even if we’ve never named them. The model doesn’t care whether we call something “raptor” or not. It cares only whether the same group of features — “can fly,” “can hunt” — appear together often enough. Implications The consequences are powerful. With this method, AI can: • Reconstruct medical knowledge (like which diseases cause which symptoms), • Infer language policies (like which countries speak French and German), • And detect hidden groupings in data — not by being told what to look for, but by observing patterns on its own. And all of this without needing a predefined human ontology. For more info, read: https://lnkd.in/e_aiMX8a by Bo X. and Steffen Staab.

Knowledge Graphs (KGs) have long been the unsung heroes behind technologies like search engines and recommendation systems. They store structured relationships between entities, helping us connect the dots in vast amounts of data. But with the rise of LLMs, KGs are evolving from static repositories into dynamic engines that enhance reasoning and contextual understanding. This transformation is gaining significant traction in the research community. Many studies are exploring how integrating KGs with LLMs can unlock new possibilities that neither could achieve alone. Here are a couple of notable examples: • 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐃𝐞𝐞𝐩𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Researchers introduced a framework called 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐫𝐚𝐩𝐡 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐀𝐠𝐞𝐧𝐭 (𝐊𝐆𝐋𝐀). By integrating knowledge graphs into language agents, KGLA significantly improved the relevance of recommendations. It does this by understanding the relationships between different entities in the knowledge graph, which allows it to capture subtle user preferences that traditional models might miss. For example, if a user has shown interest in Italian cooking recipes, the KGLA can navigate the knowledge graph to find connections between Italian cuisine, regional ingredients, famous chefs, and cooking techniques. It then uses this information to recommend content that aligns closely with the user’s deeper interests, such as recipes from a specific region in Italy or cooking classes by renowned Italian chefs. This leads to more personalized and meaningful suggestions, enhancing user engagement and satisfaction. (See here: https://lnkd.in/e96EtwKA) • 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠: Another study introduced the 𝐊𝐆-𝐈𝐂𝐋 𝐦𝐨𝐝𝐞𝐥, which enhances real-time reasoning in language models by leveraging knowledge graphs. The model creates “prompt graphs” centered around user queries, providing context by mapping relationships between entities related to the query. Imagine a customer support scenario where a user asks about “troubleshooting connectivity issues on my device.” The KG-ICL model uses the knowledge graph to understand that “connectivity issues” could involve Wi-Fi, Bluetooth, or cellular data, and “device” could refer to various models of phones or tablets. By accessing related information in the knowledge graph, the model can ask clarifying questions or provide precise solutions tailored to the specific device and issue. This results in more accurate and relevant responses in real time, improving the customer experience. (See here: https://lnkd.in/ethKNm92) By combining structured knowledge with advanced language understanding, we’re moving toward AI systems that can reason in a more sophesticated way and handle complex, dynamic tasks across various domains. How do you think the combination of KGs and LLMs is going to influence your business?

While much of the buzz around #LLMs focuses on their natural language based conversational capabilities, their true potential extends far beyond chat. These models are not just tools for predicting the next word; they are engines on which to build reasoning and analysis tools that can transform pharmaceutical #RnD and real-world evidence (#RWE) research. In this article, I explore how LLMs are reshaping the industry through: 🧩 Feature selection: e.g., in identifying critical predictors for disease models and patient stratification. 🎯 Zero shot and few-shot learning: e.g., in accelerating the creation of clinical trial protocols or adverse event reports. 🧠 Relationship extraction with semantic reasoning: e.g., in automating the construction of knowledge graphs and extraction of insights to drive drug discovery and RWE research. If you're curious about how LLMs are empowering pharma companies to make faster, data-driven decisions, read on.

Are you tired of LLMs providing you generic answers with little or sometimes no contextual alignment and thus poor domain adoption? University of California, Berkeley researchers have pioneered a novel training method, RAFT (Retrieval Augmented Fine-Tuning), which bolsters a language model’s ability to respond to domain-specific queries using an “open-book” technique. This prompts us to ponder - Do language models truly comprehend and infer from provided documents, or do they simply memorize and echo information? The Challenge of Domain-Specific Question Answering - The task of tailoring large language models (LLMs) to respond to queries in specialized areas, such as biomedical research or API documentation, is an expanding yet demanding endeavor. Conventional techniques encompass:-  👉Retrieval-augmented generation (RAG): Supplying pertinent documents to the model during inference.  👉Supervised fine-tuning on domain-specific data. 📍Nonetheless, RAG in isolation doesn’t fully leverage the potential for in-domain learning, while standard fine-tuning doesn’t coach the model to effectively utilize retrieved documents. 💫The Fusion of the Best Approaches. RAFT overcomes these shortcomings by fine-tuning the model to respond to queries using a blend of relevant & irrelevant documents. Its key attributes include:- 👩🏽💻Training on a mix of question-document pairs, some with the “oracle” document that holds the answer and some with only “distractor” documents.  Generating responses in a chain-of-thought style that cites the pertinent sections of the reference documents. ✨Remarkable Outcomes Across Diverse Domains - The researchers tested RAFT on multiple question-answering datasets, covering Wikipedia articles, biomedical papers, and API documentation. In all these specialized domains, RAFT consistently surpassed both standard fine-tuning and RAG benchmarks. 🌟Significantly, RAFT achieved substantial improvements of up to 35% on the HotpotQA Wikipedia dataset and 76% on the Torch Hub API documentation dataset compared to the base RAG model. These outcomes validate RAFT’s capacity to genuinely comprehend and infer from domain-specific documents. ⚡️Way forward towards Efficient Domain Adaptation - RAFT represents a thrilling progression towards more proficient and effective customization of language models to specialized domains. By learning to selectively read and cite pertinent information from domain-specific documents, RAFT lays the groundwork for compact, dedicated models that can compete with much larger generic language models on niche question-answering tasks. As the need for deploying LLMs to domain-specific applications continues to surge, methods like RAFT is likely to be vital for facilitating practical, cost-efficient solutions! Kudos to Tianjun Zhang Shishir Patil @Naman Jain Sheng Shen Matei Zaharia Ion Stoica Joseph E. Gonzalez for this amazing work! #llm #ai #aiadoption #genai