Cost-Effective Verification Solutions

Can we save inference cost by routing easier questions to cheaper LLMs? 🤔 📝 New research from Carnegie Mellon University, Google DeepMind, Indian Institute of Technology, Delhi and University of Southern California proposes AutoMix - an approach that strategically routes queries to larger LLMs based on the approximate correctness of outputs from a smaller LLM. 🔎 How it works: 1️⃣ First, generate an answer with a small, efficient model. 2️⃣ Then automatically verify the answer using few-shot learning. 3️⃣ Use a meta-verifier to evaluate reliability of answer. 4️⃣ Only if uncertain, invoke a larger, more accurate (but costly) model. 💡 Key benefits: • Enhances the incremental benefit per cost by up to 89% • Builds on and outperforms prior methods like FrugalGPT • Works with open source and black-box LLMs alike I'm excited by the potential for novel methods like this to make LLM solutions more cost effective, and therefore usable at scale 💰 🧵Read the paper for all the details! 👉 https://lnkd.in/eW7Nf2rf

Picture this: You're tasked with checking if a username exists in a database of ONE BILLION strings. Traditional approaches would mean either: 1. Loading everything into memory (goodbye RAM!) 2. Making expensive database hits for EVERY single query This was my friend's challenge last year when scaling a user verification system. Query times were skyrocketing, and the infrastructure costs followed. Bloom Filters – the elegant solution I break down in my latest video: "Bloom Filter and Probabilistic Data Structure in detail | Scalable Thinking" With just a few KB of memory and some clever probability math, you can reduce the lookup times by 95% and eliminate unnecessary database hits. The trade-off? A tiny percentage of false positives that the system could easily handle with secondary verification. 🔍 Why you should care about Bloom Filters: - Space-efficient solution for membership testing - Dramatically faster than traditional lookups - Perfect for high-scale systems where false positives are acceptable - Essential knowledge for any engineer working on performance-critical applications Whether you're designing a cache, building a crawler, or optimizing a database with millions (or billions!) of records, understanding probabilistic data structures isn't just nice-to-have—it's becoming essential knowledge for modern software engineering. Check out the full breakdown in my video, Link in the first comment #SoftwareEngineering #SystemDesign #DataStructures #Performance #ScalableSystems #BloomFilters

Could #ZKP bring the ultimate privacy-friendly and cost-efficient KYC-solution for both institutions and customers?   🔍 What is Zero-Knowledge Proof (ZKP)?   ZKP is a cryptographic method that allows one party (the prover) to prove to another party (the verifier) that a statement is true, without revealing any specific information about the statement itself. For example: verify you are of legal age to purchase a beer, without the vendor being able to see your date of birth.   🌐 ZKP in the Digital World   In the realm of digital security and transactions, ZKP can be used to verify the authenticity of information without revealing the data itself. For example: prove you live in a specific country or area, without disclosing your private home address.   ⁉ Okay, wild thought, could it be possible that creating a bank-account could be performed entirely using ZKP?   💡 Introducing zkKYC   Know Your Customer (KYC) processes are essential in the business world, especially within the financial sector. They help companies verify the identity of their clients, ensuring transparency and compliance with regulations, minimizing fraud risks. Traditional KYC-methods often require detailed personal information, which can be at risk if the company's data storage is compromised.   This is where zkKYC comes into play, which leverages the principles of ZKP to create a system where users can verify their identities without revealing their actual personal data.   📊 Advantages of zkKYC   - Enhanced Privacy - Regulatory Compliance - Efficiency and - Reduced Costs   Individuals no longer need to expose their personal details to companies, mitigating the risk of data breaches. Since there's no need to transfer, store and verify sensitive data, the process can be faster and more streamlined. Businesses can save on many big costs. zkKYC can be designed to meet regulatory requirements, ensuring that businesses stay compliant without sacrificing user privacy.   ✨ The Future of Digital Identity Verification   As digital interactions continue to dominate our daily lives, demand increases for systems that prioritize security and privacy. With more industries adopting blockchain technology and other advanced cryptographic methods, it excites me to foresee a future where you never have to hand over your sensitive personal data to prove your identity. Instead, cryptographic assurances will vouch for the authenticity of your claims, ensuring a safer and more private digital ecosystem for all.   📚 While the intricacies of ZKP might seem daunting to those unfamiliar with cryptography, its potential applications make it worth understanding and exploring. Need help? Let me know. 🗓 Also, October 5th I will be at the PECB Conference in Paris discussing this topic.   Please let me know whether you think institutions will embrace ZKP for KYC-processes ⤵   #DigitalIdentity #PrivacyMatters

This DeepSeek R1's — Reinforcement Learning Algorithm is the single most important reason why the model is so cheap. DeepSeek R1 is a reinforcement learning model, and its training loop follows a standard RL structure(Step-1): - Generate output from the model - Evaluate the output for accuracy - Reward or penalize the model based on accuracy - Iterate billions of times DeepSeek R1 evaluates accuracy at scale—the fundamental reason it is so cost-effective. But the question is — How do we efficiently evaluate model accuracy at the scale of billions? The conventional approach uses another large AI model to determine response accuracy, which is expensive and computationally intensive. But DeepSeek R1’s Approach is different, instead of brute-force evaluation, DeepSeek optimizes the process across three dimensions(step 2): - Grouping Evaluations – Evaluations are batched intelligently rather than performed individually. Since training runs into the billions, reducing evaluation volume by 80% translates directly into an 80% reduction in GPU cost. - Cheaper Evaluation Mechanisms – DeepSeek embeds reasoning within <think> tags, enabling simple pattern matching (Ctrl+F) instead of invoking another LLM for verification. It also executes code or computes outputs directly—far cheaper and more reliable than neural network-based validation. - Smaller Evaluation Models – With steps 1 and 2 reducing the evaluation load, DeepSeek can downsize the LLM responsible for output assessment. This smaller model is arguably the single biggest cost-saving factor in the entire pipeline. This is how the reinforcement learning system systematically avoids unnecessary computing and converges on accurate outputs at a fraction of the usual cost. Efficient evaluation, not brute-force verification, is the key.