AI Language Processing

Large Language Models (LLMs) are powerful, but how we 𝗮𝘂𝗴𝗺𝗲𝗻𝘁, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 them truly defines their impact. Here's a simple yet powerful breakdown of how AI systems are evolving: 𝟭. 𝗟𝗟𝗠 (𝗕𝗮𝘀𝗶𝗰 𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲)   ↳ This is where it all started. You give a prompt, and the model predicts the next tokens. It's useful — but limited. No memory. No tools. Just raw prediction. 𝟮. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻)   ↳ A significant leap forward. Instead of relying only on the LLM’s training, we 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗳𝗿𝗼𝗺 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 (like vector databases). The model then crafts a much more relevant, grounded response.   This is the backbone of many current AI search and chatbot applications. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠𝘀 (𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 + 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲)   ↳ Now we’re entering a new era. Agent-based systems don’t just answer — they think, plan, retrieve, loop, and act.   They: - Use 𝘁𝗼𝗼𝗹𝘀 (APIs, search, code) - Access 𝗺𝗲𝗺𝗼𝗿𝘆 - Apply 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗰𝗵𝗮𝗶𝗻𝘀 - And most importantly, 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝗻𝗲𝘅𝘁 These architectures are foundational for building 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀, 𝗰𝗼𝗽𝗶𝗹𝗼𝘁𝘀, 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀. The future is not just about 𝘸𝘩𝘢𝘵 the model knows, but 𝘩𝘰𝘸 it operates. If you're building in this space — RAG and Agent architectures are where the real innovation is happening.

You see a new NLP breakthrough paper and think, This could change everything. But most of these breakthroughs never leave the lab. Why? There's a big gap between research and product. Academic NLP is all about optimizing metrics, proving new ideas, chasing novelty. But enterprise buyers? They care about reliability, scalability, and solving a real pain point. A model that crushes benchmarks in the lab often breaks down in the messy real world. Data is noisy. Requirements shift. Stakeholders want clear ROI, not just accuracy boosts. So what actually bridges the gap? You need people who understand both worlds. Product leaders who can take a research prototype, stress test it in production, and adapt it for real business workflows. You need to involve engineers, product managers, and even sales early, not just when the tech is ready. And you need to validate early, with real users and real-world data. The best NLP products didn't start as flawless algorithms. They started as gritty experiments, built with customers, iterated fast, and constantly translated research into practical value. That's how you take a breakthrough from paper to market.

To Believe or Not to Believe Your LLM We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider both epistemic and aleatoric uncertainties, where the former comes from the lack of knowledge about the ground truth (such as about facts or the language), and the latter comes from irreducible randomness (such as multiple possible answers). In particular, we derive an information-theoretic metric that allows to reliably detect when only epistemic uncertainty is large, in which case the output of the model is unreliable. This condition can be computed based solely on the output of the model obtained simply by some special iterative prompting based on the previous responses. Such quantification, for instance, allows to detect hallucinations (cases when epistemic uncertainty is high) in both single- and multi-answer responses. This is in contrast to many standard uncertainty quantification strategies (such as thresholding the log-likelihood of a response) where hallucinations in the multi-answer case cannot be detected. We conduct a series of experiments which demonstrate the advantage of our formulation. Further, our investigations shed some light on how the probabilities assigned to a given output by an LLM can be amplified by iterative prompting, which might be of independent interest.

If you’re an AI engineer working on fine-tuning LLMs for multi-domain tasks, you need to understand RLVR. One of the biggest challenges with LLMs today isn’t just performance in a single domain, it’s generalization across domains. Most reward models tend to overfit. They learn patterns, not reasoning. And that’s where things break when you switch context. That’s why this new technique, RLVR with Cross-Domain Verifier, caught my eye. It builds on Microsoft’s recent work, and it’s one of the cleanest approaches I’ve seen for domain-agnostic reasoning. Here’s how it works, step by step 👇 ➡️ First, you train a base model with RLVR, using a dataset of reasoning samples (x, a), and a teacher grader to help verify whether the answers are logically valid. This step builds a verifier model that understands reasoning quality within a specific domain. ➡️ Then, you use that verifier to evaluate exploration data - which includes the input, the model’s reasoning steps, and a final conclusion. These scores become the basis for training a reward model that focuses on reasoning quality, not just surface-level output. The key here is that this reward model becomes robust across domains. ➡️ Finally, you take a new reasoning dataset and train your final policy using both the reward model and RLVR again - this time guiding the model not just on task completion, but on step-wise logic that holds up across use cases. 💡 The result is a model that isn’t just trained to guess the answer, it’s trained to reason through it. That’s a game-changer for use cases like multi-hop QA, agentic workflows, and any system that needs consistent logic across varied tasks. ⚠️ Most traditional pipelines confuse fluency with correctness. RLVR fixes that by explicitly verifying each reasoning path. 🔁 Most reward models get brittle across domains. This one learns from the logic itself. 〰️〰️〰️〰️ ♻️ Share this with your network 🔔 Follow me (Aishwarya Srinivasan) for more data & AI insights

You’re in an AI Engineer interview. Interviewer asks: How do you handle multi language prompting effectively? Most people jump to translation APIs. Strong answer goes deeper. 1. Detect language first Never assume. Identify the user’s language and script before prompting. 2. Preserve intent, not just words Literal translation often breaks tone, context, and business meaning. 3. Prompt in the user’s language when possible Models usually respond better when instructions and output language align. 4. Use English for complex reasoning, then localize output For harder logic tasks, reasoning in English + final response in target language often works better. 5. Handle mixed language inputs Real users switch languages mid sentence. Your system should too. 6. Keep terminology consistent Especially for healthcare, finance, legal, and product names. 7. Test by language, not globally Kannada, Hindi, Tamil, Japanese, Arabic, Spanish all fail differently. 8. Build fallback layers If confidence is low, ask clarifying questions instead of hallucinating. What interviewers want to hear: You understand that multilingual AI is a product problem, not just a translation problem. #AI #GenerativeAI #PromptEngineering #LLM #AIEngineer #MachineLearning #NLP #AIEngineering Follow Sneha Vijaykumar for more... 😊

❓ If we ask a multilingual language model a factual question written on different languages, do the answers always refer to the same entity? well..not quite. 🤔 I'm happy to report that our '24 Summer Research Intern Mahardika Krisna Ihsani from @MBZUAI collaboration came to fruition in joint work with Barid Xi Ai! We study crossling consistency across LLMs 🌎🌍🌏. See the ❇️EMNLP Findings🎇preprint https://t.co/zyo37zV9r6 & thread 🧵 for details! In our work, we did the evaluation on code-switched sentence and we expect that by this setting, the model aligns the knowledge in more language-agnostic fashion. We limited scope to only consider English as the pivot language and we examined the top-5 answers rather than top-1. We discovered that query whose language is distinct from the pivot language could elicit model to answer in different entity. This finding is substantially pronounced when the writing script is different than the pivot language. Additionally, we could see that larger model doesnt give substantial consistency improved and we explored why this happened. So we examined the cross-lingual consistency across layer and we discovered that there is no monotonic improvement and this could possibly explain why. Lastly, we also tried several methods to alleviate the inconsistency bottleneck. Among the other methods, we found that training objective that promotes cross-lingual alignment shows the best improvement and alleviates bottleneck as shown by the result of xlm-align and xlm-r-cs. If you're keen to know more about the details, please check out the preprint: https://lnkd.in/gv2gb6zh. Huge thanks to the co first authors Mahardika Krisna Ihsani and Barid Xi Ai.

Like a fortress growing taller but keeping the same cracks, large language models may be expanding without becoming safer. A collaborative study between the UK AI Security Institute, Anthropic, University of Oxford, and the The Alan Turing Institute exposes this unsettling symmetry. The study demonstrates that data poisoning does not dilute with scale. Even as models and datasets grow by orders of magnitude, the absolute number of poisoned samples required to implant a backdoor remains roughly constant. In their experiments, 250 poisoned documents were sufficient to compromise models ranging from 600M to 13B parameters, despite the largest model being trained on nearly twenty times more clean data. This overturns the long-held belief that increasing data volume would naturally “average out” adversarial noise. Instead, larger models appear to be more sample-efficient learners, capable of internalizing both useful and malicious signals with equal precision. For those of us working on trust layers over model training - through Knowledge Graphs, ontology-driven provenance, and dynamic data vetting - this finding reinforces a critical point: robustness is not an emergent property of scale; it must be deliberately engineered. Key implications include: 1) Scaling laws for capability may mirror scaling laws for vulnerability. 2) Fine-tuning or alignment processes cannot reliably erase deeply embedded backdoors; they often only suppress them. 3) Graph-based reasoning layers may become essential for tracing data lineage and identifying subtle poisoning patterns before training. In the pursuit of larger and more capable models, the real challenge is ensuring that every data point shaping them remains interpretable, auditable, and trusted. Scaling safety will demand more than data volume - it will require transparency, traceability, and semantic intelligence across the entire data pipeline. Full length article: https://lnkd.in/gmMNdFgF #AISafety #DataPoisoning #ModelRobustness #BackdoorAttacks #AdversarialAI #AICybersecurity #LLMSecurity #AITrust #AIIntegrity #ResponsibleAI #ScalingLaws #FoundationModels #LargeLanguageModels #ModelAlignment #AIAlignment #ModelScaling #AIResearch #MachineLearningResearch #KnowledgeGraphs #OntologyEngineering #DataLineage #DataProvenance #TrustworthyAI #ExplainableAI #InterpretableAI #SemanticAI #AIEthics #AIGovernance #SafeAI #AITransparency #AIForGood #TechPolicy #DigitalTrust #FutureOfAI #AI #MachineLearning #DeepLearning #GenerativeAI #TechInnovation #EmergingTech

Most people think AI understands their questions. But here's the reality: LLMs don’t actually understand like humans do. They predict. When you ask an AI: Explain why sales dropped last quarter Here’s what actually happens behind the scenes: Step 1 ~ Your question is converted into numbers The AI converts your text into tokens and embeddings. Step 2 ~ The model searches patterns It compares your question with patterns learned from massive datasets. Step 3 ~ Context matters The AI looks at: • Your prompt • Previous conversation • System instructions • Retrieved documents (if using RAG) Step 4 ~ Probability-based prediction The AI predicts the most likely next word Then the next… Then the next… Until a full response is generated. So technically… AI is not thinking AI is predicting intelligently This is why: • Prompt quality matters • Context matters • Data matters • Instructions matter Same question + Different context = Different answers That’s also why AI sometimes: • Hallucinates • Misses business context • Sounds confident but wrong Because it's predicting… not reasoning like humans. But here’s the interesting part: Even though it's prediction-based, LLMs are becoming powerful enough to: • Analyze documents • Generate insights • Assist decision making • Automate workflows We're moving from: Search Engines → AI Assistants → AI Agents Understanding how LLMs respond to queries is the first step to building reliable AI systems. Because good AI systems aren't built on models alone. They’re built on: Context + Data + Prompts + Logic And that’s where real AI engineering begins. #AI #LLM #GenerativeAI #DataAnalytics #AIEngineering #PromptEngineering #DataScience #AIAgent

The challenge of integrating multiple large language models (LLMs) in enterprise AI isn’t just about picking the best model, it’s about choosing the right mix for each specific scenario. When I was tasked with leveraging Azure AI Foundry alongside Microsoft 365 Copilot, Copilot Studio, Claude Sonnet 4, and Opus 4.1 to enhance workflows, the advice I heard was to double down on a single, well‑tuned model for simplicity. In our environment, that approach started to break down at scale. Model pluralism turned out to be the unexpected solution, using multiple LLMs in parallel, each optimised for different tasks. The complexity was daunting at first, from integration overhead to security and governance concerns. But this approach let us tighten data grounding and security in ways a single model couldn’t. For example, routing the most sensitive tasks to Opus 4.1 helped us measurably reduce security exposure in our internal monitoring, while Claude Sonnet 4 noticeably improved the speed and quality of customer‑facing interactions. In practice, the chain looked like this: we integrated multiple LLMs, mapped each one to the tasks it handled best, and saw faster execution on specialised workloads, fewer security and compliance issues, and a clear uplift in overall workflow effectiveness. Just as importantly, the architecture became more robust, if one model degraded or failed, the others could pick up the slack, which matters in a high‑stakes enterprise environment. The lesson? The “obvious” choice, standardising on a single model for simplicity, can overlook critical realities like security, governance, and scalability. Model pluralism gave us the flexibility and resilience we needed once we moved beyond small pilots into real enterprise scale. For those leading enterprise AI initiatives, how are you balancing the trade‑off between operational simplicity and a pluralistic, multi‑model architecture? What does your current model mix look like?

Exciting New Research: Injecting Domain-Specific Knowledge into Large Language Models I just came across a fascinating comprehensive survey on enhancing Large Language Models (LLMs) with domain-specific knowledge. While LLMs like GPT-4 have shown remarkable general capabilities, they often struggle with specialized domains such as healthcare, chemistry, and legal analysis that require deep expertise. The researchers (Song, Yan, Liu, and colleagues) have systematically categorized knowledge injection methods into four key paradigms: 1. Dynamic Knowledge Injection - This approach retrieves information from external knowledge bases in real-time during inference, combining it with the input for enhanced reasoning. It offers flexibility and easy updates without retraining, though it depends heavily on retrieval quality and can slow inference. 2. Static Knowledge Embedding - This method embeds domain knowledge directly into model parameters through fine-tuning. PMC-LLaMA, for instance, extends LLaMA 7B by pretraining on 4.9 million PubMed Central articles. While offering faster inference without retrieval steps, it requires costly updates when knowledge changes. 3. Modular Knowledge Adapters - These introduce small, trainable modules that plug into the base model while keeping original parameters frozen. This parameter-efficient approach preserves general capabilities while adding domain expertise, striking a balance between flexibility and computational efficiency. 4. Prompt Optimization - Rather than retrieving external knowledge, this technique focuses on crafting prompts that guide LLMs to leverage their internal knowledge more effectively. It requires no training but depends on careful prompt engineering. The survey also highlights impressive domain-specific applications across biomedicine, finance, materials science, and human-centered domains. For example, in biomedicine, domain-specific models like PMC-LLaMA-13B significantly outperform general models like LLaMA2-70B by over 10 points on the MedQA dataset, despite having far fewer parameters. Looking ahead, the researchers identify key challenges including maintaining knowledge consistency when integrating multiple sources and enabling cross-domain knowledge transfer between distinct fields with different terminologies and reasoning patterns. This research provides a valuable roadmap for developing more specialized AI systems that combine the broad capabilities of LLMs with the precision and depth required for expert domains. As we continue to advance AI systems, this balance between generality and specialization will be crucial.