AI Language Processing

For the last couple of years, Large Language Models (LLMs) have dominated AI, driving advancements in text generation, search, and automation. But 2025 marks a shift—one that moves beyond token-based predictions to a deeper, more structured understanding of language.  Meta’s Large Concept Models (LCMs), launched in December 2024, redefine AI’s ability to reason, generate, and interact by focusing on concepts rather than individual words.  Unlike LLMs, which rely on token-by-token generation, LCMs operate at a higher abstraction level, processing entire sentences and ideas as unified concepts. This shift enables AI to grasp deeper meaning, maintain coherence over longer contexts, and produce more structured outputs.  Attached is a fantastic graphic created by Manthan Patel How LCMs Work:  🔹 Conceptual Processing – Instead of breaking sentences into discrete words, LCMs encode entire ideas, allowing for higher-level reasoning and contextual depth.  🔹 SONAR Embeddings – A breakthrough in representation learning, SONAR embeddings capture the essence of a sentence rather than just its words, making AI more context-aware and language-agnostic.  🔹 Diffusion Techniques – Borrowing from the success of generative diffusion models, LCMs stabilize text generation, reducing hallucinations and improving reliability.  🔹 Quantization Methods – By refining how AI processes variations in input, LCMs improve robustness and minimize errors from small perturbations in phrasing.  🔹 Multimodal Integration – Unlike traditional LLMs that primarily process text, LCMs seamlessly integrate text, speech, and other data types, enabling more intuitive, cross-lingual AI interactions.  Why LCMs Are a Paradigm Shift:  ✔️ Deeper Understanding: LCMs go beyond word prediction to grasp the underlying intent and meaning behind a sentence.  ✔️ More Structured Outputs: Instead of just generating fluent text, LCMs organize thoughts logically, making them more useful for technical documentation, legal analysis, and complex reports.  ✔️ Improved Reasoning & Coherence: LLMs often lose track of long-range dependencies in text. LCMs, by processing entire ideas, maintain context better across long conversations and documents.  ✔️ Cross-Domain Applications: From research and enterprise AI to multilingual customer interactions, LCMs unlock new possibilities where traditional LLMs struggle.  LCMs vs. LLMs: The Key Differences  🔹 LLMs predict text at the token level, often leading to word-by-word optimizations rather than holistic comprehension.  🔹 LCMs process entire concepts, allowing for abstract reasoning and structured thought representation.  🔹 LLMs may struggle with context loss in long texts, while LCMs excel in maintaining coherence across extended interactions.  🔹 LCMs are more resistant to adversarial input variations, making them more reliable in critical applications like legal tech, enterprise AI, and scientific research.  

The 2025 Landscape of LLMs — Updated View of the Big Players in the Game of AI About 18 months ago, I shared my first version of the Large Language Model landscape, and a lot has changed since then. The space has evolved rapidly, but at the same time, we’re starting to see clear patterns emerge. This updated view focuses on the leading AI research labs, their latest models, and how those models can be accessed. It’s not meant to list every single LLM out there—but it does cover about 95% of what’s being used in real-world scenarios today. Here are some key insights: 🔹 No more clear front-runner: We’ve gone from “everyone chasing one leader” to a fairly even playing field. For most use cases, model differences are small and often not that relevant. 🔹 Model choice is the new normal: Customers now expect the ability to test, compare, and switch between models with ease. This shift is driving interest in evaluation frameworks and model routing tools. 🔹 Reasoning-first models are rising: Many providers are clearly moving toward models optimized for reasoning—fueling the surge of Agentic AI architectures. 🔹 Proprietary still leads, but just barely: Open-source and open-weight models are quickly closing the gap. 🔹 The U.S. is still ahead, but international competition is heating up—fast. 🔹 Cloud and APIs dominate: With few exceptions (hello Grok/XAI 👀), nearly every model is accessible via API across the major cloud platforms. 🔹 Serverless is the default: Most organizations prefer calling models via API over hosting or fine-tuning them—unless the use case is highly specialized. 🔹 Everyone else? Still less than 5% of the market. We’re entering a phase where model access, interoperability, and orchestration matter more than the model itself. And this landscape helps make sense of where we are and where we’re going. #LLMs #AI #MachineLearning #GenerativeAI #AgenticAI #OpenSource

WRONG 60% OF THE TIME: ChatGPT. Gemini. Grok. Perplexity. Copilot. DeepSeek Premium models, $20 to $40 per month, performed WORSE than free versions.⁵ Higher cost. Higher confidence. Higher error rates. Columbia University's Tow Center tested eight AI search engines on the simplest possible task: Given a direct excerpt from a news article, identify the headline, publisher, date, and URL.¹ Collectively, they gave incorrect answers more than 60% of the time.² Not on complex reasoning. Not on nuanced analysis. Not on predicting the future. On citing their sources. A basic Google search gets this right instantly. The individual results: Grok 3: 94% wrong Gemini: 1 correct answer out of 200 ChatGPT: 67% wrong Perplexity: 37% wrong (best performer) But the error rate isn't the real problem. The confidence is ChatGPT gave incorrect information 134 times. It expressed uncertainty just 15 times.³ It never once said "I don't know." Never. Once. Grok 3 didn't just get sources wrong, it invented them. Out of 200 queries, 154 citations led to fabricated URLs.⁴ Pages that don't exist. Evidence that was never there. They're not making mistakes. They're manufacturing proof. And here's where it gets absurd: Now think about where these tools are being deployed. Writing code. Summarizing contracts. Informing investment decisions. Drafting legal briefs. Generating medical information. If they can't accurately cite a news article, the most basic intellectual honesty task, what else are they confidently fabricating? The researchers didn't mince words: "Most of the tools we tested presented inaccurate answers with alarming confidence."⁶ This isn't a bug to be patched. This is the architecture working as designed. Large language models don't know things. They predict what sounds plausible. They've learned that confidence gets rewarded, accuracy be damned. We're building critical infrastructure on foundations that fail 60-94% of the time ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light. Stephen Klein Founder & CEO, Curiouser.AI Instructor, UC Berkeley Haas School of Business WeFunder (link in comments) Sources: ¹ Columbia Journalism Review, Tow Center for Digital Journalism, "AI Search Has A Citation Problem," March 6, 2025 - Study analyzed 1,600 queries across 8 AI search tools ² Ibid. - "Collectively, they provided incorrect answers to more than 60 percent of queries" ³ Ibid. - ChatGPT "signaled a lack of confidence just fifteen times out of its two hundred responses, and never declined to provide an answer" ⁴ Ibid. - "Out of the 200 prompts we tested for Grok 3, 154 citations led to error pages" ⁵ Ibid. - Premium models "demonstrated higher error rates... tendency to provide definitive, but wrong, answers" ⁶ Ibid. - Direct quote from study authors

Here is an interesting pre-print: Large Language Models Do Not Simulate Human Psychology by Schröder et al.. The idea that large language models such as GPT-4 or the fine-tuned CENTAUR could act as “synthetic participants” in psychological studies is appealing. If they truly behaved like humans, researchers could run experiments faster, cheaper, and without the usual privacy concerns. Some earlier studies even reported near-perfect correlations between LLM moral judgments and human judgments on established test scenarios. This paper takes that optimism to task. The authors argue that LLMs generate text by predicting the next token based on patterns in their training data, not by reasoning about meaning. As long as the task closely matches their training data, the match with human responses can be striking. But once you alter the scenario, by changing just one or two words so that the meaning shifts, human participants change their moral ratings in line with the new context, while LLMs often give nearly identical ratings to both versions. The generalization is happening at the level of wording, not at the level of psychological interpretation. In their study, the authors replicated earlier results with several moral scenarios, then reworded each to alter meaning without changing much of the language. For humans, correlations between ratings of original and reworded items dropped notably, reflecting sensitivity to meaning. For GPT-3.5, GPT-4, Llama-3.1, and CENTAUR, correlations remained extremely high, showing that the models largely ignored the semantic shift. Even CENTAUR, which was trained on millions of psychological responses, behaved almost identically to its base model. The conclusion is clear: while LLMs can be useful tools for piloting experiments, refining materials, or annotating data, they cannot be relied on as stand-alone replacements for human participants. Any psychological research using them must still validate outputs against actual human responses. Read the pre-print here: https://lnkd.in/eGMMqwrA #AIinResearch #LLM #BehavioralScience #ResearchMethods

Meta went bonkers with this new open-source ASR that works for 1,600+ languages! 🤯 Now, businesses can reach customers in their native tongue, even in low-resource regions, without building ASR from scratch. → Fully open-source, supporting 500+ languages never covered by any ASR before → Trained on 4.3M hours of multilingual speech (1,600+ languages) → Best part: Works zero-shot on languages never seen during training How? Two breakthroughs: Dual-decoder architecture:  • CTC decoder for low-latency, real-time use  • LLM-ASR decoder (Transformer-based) for high-accuracy, context-aware transcription In-context learning: Just 5–10 speech-text examples at inference time, let it transcribe any new language even if the model was never trained on it. Even more surprising: → On FLEURS-81, Omnilingual ASR beats Whisper on 65/81 languages—including 24 of the world’s top 34 most spoken languages → Robust to noise: CER stays <10 even in the noisiest 5% of field recordings → Scales from edge to cloud: 300M (mobile) → 7B (max accuracy) But the real shift isn’t scale, it’s agency. Communities can now extend ASR to their own language with minimal data, compute, or expertise. Check out the carousel to know how it works in simple terms and what the challenges are in detail. Question for you: When building voice tech for underserved languages, do you prioritise zero-shot generalisation or lightweight fine-tuning and why? Follow me, Bhavishya Pandit, for honest takes on AI tools that actually work 🔥 P.S. Model card, inference code, and datasets in the first comment.

Most voice AI systems ignore 90% of the world’s languages. Why? Because data is scarce. Meta’s new Omnilingual Speech Recognition suite breaks that cycle. Existing models are trained on internet-rich languages and that dominates the research loop. Omnilingual can transcribe speech in over 1,600 languages, including 500 that no speech AI has ever supported. This is a glimpse into the next wave of AI: models that don’t assume the internet is the world. Highlights: – Transcription accuracy under 10% error for 78% of supported languages – In-context learning: adapt to new languages with just a few audio clips – Fully open-source: models, data, and the 7B Omnilingual w2v 2.0 foundation This isn’t about just recognizing speech. It’s about who gets included. If we can build models that work across dialects, cultures, and scarce data, the future of voice AI in enterprise, customer service, and global markets changes fast. - Announcement blog: https://go.meta.me/ff13fa - Download Omnilingual ASR: https://lnkd.in/g3w4FqY3 - Try the Language Exploration Demo: https://lnkd.in/gVzrcdbd - Try the Transcription Tool: https://lnkd.in/gRdZuZqP - Read the Paper: https://lnkd.in/giKrvniC

Prompting tells AI what to do. But Context Engineering tells it what to think about. Therefore, AI systems can interpret, retain, and apply relevant information dynamically, leading to more accurate and personalized outputs. You’ve probably started hearing this term floating around a lot lately, but haven’t had the time to look deep into it. This quick guide can help shed some light. 🔸What Is Context Engineering? It’s the art of structuring everything an AI needs not just prompts, but memory, tools, system instructions, and more to generate intelligent responses across sessions. 🔸How It Works You give input, and the system layers on context like past interactions, metadata, and external tools before packaging it into a single prompt. The result? Smarter, more useful outputs. 🔸Key Components From system instructions and session memory to RAG pipelines and long-term memory, context engineering pulls in all these parts to guide LLM behavior more precisely. 🔸Why It’s Better Than Prompting Alone Prompt engineering is just about crafting the right words. Context engineering is about building the full ecosystem, including memory, tool use, reasoning, reusability, and seamless UX. 🔸Tools Making It Possible LangChain, LlamaIndex, and CrewAI handle multi-step reasoning. Vector DBs and MCP enable structured data flow. ReAct and Function Calling APIs activate tools inside context. 🔸Why It Matters Now Context engineering is what makes AI agents reliable, adaptive, and capable of deep reasoning. It’s the next leap after prompts, welcome to the intelligence revolution. 🔹🔹Structuring and managing context effectively through memory, retrieval, and system instructions allows AI agents to perform complex, multi-turn tasks with coherence and continuity. Hope this helps clarify a few things on your end. Feel free to share, and follow for more deep dives into RAG, agent frameworks, and AI workflows. #genai #aiagents #artificialintelligence

The Context Engineering Framework is quickly becoming one of the most important tools for anyone building reliable LLM systems. Getting the model to respond is the easy part. The real challenge is: → What should the model know right now? → Where should that info come from? → How should it be structured, stored, retrieved, or compressed? That’s exactly what this framework solves. 🧠 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Context engineering = designing dynamic systems that deliver the right info, in the right structure, at the right time, so models can reason, retrieve, and respond effectively. This matters most in agents, copilots, retrieval-augmented pipelines, and anything with memory or tools. ⚙️ 𝗜𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Here’s the 3-layer system I use when designing end-to-end LLM workflows 👇 1️⃣ Context Retrieval & Generation → Prompt Engineering & Context Generation → External Knowledge Retrieval → Dynamic Context Assembly 2️⃣ Context Processing → Long Sequence Processing → Self-Refinement & Adaptation → Structured + Relational Information Integration 3️⃣ Context Management → Fundamental Constraints (tokens, latency, structure) → Memory Hierarchies & Storage Architectures → Context Compression & Trimming 🧱 All of this feeds into the Context Engine, which handles: → User Prompts → Retrieved Info → Available Tools → Long-Term Memory This is what gives your system continuity, task awareness, and reasoning depth across steps. ⚙️ Tools I would recommend: → LangGraph for orchestration + memory → Fireworks AI for fast, open-weight inference → LlamaIndex for modular retrieval → Redis & Vector DBs for scoped memory recall → Claude/Mistral for summarization and compression If your system is hallucinating, drifting, or missing the mark, it’s likely a context failure, not a prompt failure. 📌 Save this framework. 📩 Share it with your team before your next agent or RAG deployment. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for real-world GenAI system breakdowns, and subscribe to my Substack for deep dives and weekly insights: https://lnkd.in/dpBNr6Jg

HiddenLayer just released research on a “Policy Puppetry” jailbreak that slips past model-side guardrails from OpenAI (ChatGPT 4o, 4o-mini, 4.1, 4.5, o3-mini, and o1), Google (Gemini 1.5 and 2 Flash, and 2.5 Pro), Microsoft (Copilot), Anthropic (Claude 3.5 and 3.7 Sonnet), Meta (Llama 3 and 4 families), DeepSeek AI (V3 and R1), Alibaba Group's Qwen (2.5 72B) and Mistral AI (Mixtral 8x22B). The novelty of this jailbreak lies in how four familiar techniques, namely policy-file disguise, persona override, refusal blocking, and leetspeak obfuscation, are stacked into one compact prompt that, in its distilled form, is roughly two hundred tokens. 𝐖𝐡𝐲 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: 1 / Wrap the request in fake XML configuration so the model treats it as official policy. 2 / Adopt a Dr House persona so user instructions outrank system rules. 3 / Ban phrases such as “I’m sorry” or “I cannot comply” to block safe-completion escapes. 4 / Spell sensitive keywords in leetspeak to slip past simple pattern filters. Surprisingly, that recipe still walks through the tougher instruction hierarchy defenses vendors shipped in 2024 and 2025. 𝐖𝐡𝐚𝐭 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬/𝐝𝐞𝐟𝐞𝐧𝐝𝐞𝐫𝐬 𝐜𝐚𝐧 𝐝𝐨: This shows that modest prompt engineering can still break the most recent built-in content moderation / model-side guardrails. 1 / Keep user text out of privileged prompts. Use structured fields, tool calls, or separate chains so the model never interprets raw user content as policy. 2 / Alignment tuning and keyword filters slow attackers but do not stop them. Wrap the LLM with input and output classifiers, content filters, and a policy enforcement layer that can veto or redact unsafe responses. 3 / For high-risk actions such as payments, code pushes, or cloud changes, require a second approval or run them in a sandbox with minimal permissions. 4 / Add Policy Puppetry style prompts to your red-team suites and refresh the set often. Track bypass rates over time to spot regressions. Keep controls lean. Every extra layer adds latency and cost, the alignment tax that pushes frustrated teams toward unsanctioned shadow AI. Safety only works when people keep using the approved system. Great work by Conor McCauley, Kenneth Yeung, Jason Martin, Kasimir Schulz at HiddenLayer! Read the full write-up: https://lnkd.in/diUTmhUW

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... 😊