How Llms Boost Performance

Researchers from Oxford University just achieved a 14% performance boost in mathematical reasoning by making LLMs work together like specialists in a company. In their new MALT (Multi-Agent LLM Training) paper, they introduced a novel approach where three specialized LLMs - a generator, verifier, and refinement model - collaborate to solve complex problems, similar to how a programmer, tester, and supervisor work together. The breakthrough lies in their training method: (1) Tree-based exploration - generating thousands of reasoning trajectories by having models interact (2) Credit attribution - identifying which model is responsible for successes or failures (3) Specialized training - using both correct and incorrect examples to train each model for its specific role Using this approach on 8B parameter models, MALT achieved relative improvements of 14% on the MATH dataset, 9% on CommonsenseQA, and 7% on GSM8K. This represents a significant step toward more efficient and capable AI systems, showing that well-coordinated smaller models can match the performance of much larger ones. Paper https://lnkd.in/g6ag9rP4 — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai

We know LLMs can substantially improve developer productivity. But the outcomes are not consistent. An extensive research review uncovers specific lessons on how best to use LLMs to amplify developer outcomes. 💡 Leverage LLMs for Improved Productivity. LLMs enable programmers to accomplish tasks faster, with studies reporting up to a 30% reduction in task completion times for routine coding activities. In one study, users completed 20% more tasks using LLM assistance compared to manual coding alone. However, these gains vary based on task complexity and user expertise; for complex tasks, time spent understanding LLM responses can offset productivity improvements. Tailored training can help users maximize these advantages. 🧠 Encourage Prompt Experimentation for Better Outputs. LLMs respond variably to phrasing and context, with studies showing that elaborated prompts led to 50% higher response accuracy compared to single-shot queries. For instance, users who refined prompts by breaking tasks into subtasks achieved superior outputs in 68% of cases. Organizations can build libraries of optimized prompts to standardize and enhance LLM usage across teams. 🔍 Balance LLM Use with Manual Effort. A hybrid approach—blending LLM responses with manual coding—was shown to improve solution quality in 75% of observed cases. For example, users often relied on LLMs to handle repetitive debugging tasks while manually reviewing complex algorithmic code. This strategy not only reduces cognitive load but also helps maintain the accuracy and reliability of final outputs. 📊 Tailor Metrics to Evaluate Human-AI Synergy. Metrics such as task completion rates, error counts, and code review times reveal the tangible impacts of LLMs. Studies found that LLM-assisted teams completed 25% more projects with 40% fewer errors compared to traditional methods. Pre- and post-test evaluations of users' learning showed a 30% improvement in conceptual understanding when LLMs were used effectively, highlighting the need for consistent performance benchmarking. 🚧 Mitigate Risks in LLM Use for Security. LLMs can inadvertently generate insecure code, with 20% of outputs in one study containing vulnerabilities like unchecked user inputs. However, when paired with automated code review tools, error rates dropped by 35%. To reduce risks, developers should combine LLMs with rigorous testing protocols and ensure their prompts explicitly address security considerations. 💡 Rethink Learning with LLMs. While LLMs improved learning outcomes in tasks requiring code comprehension by 32%, they sometimes hindered manual coding skill development, as seen in studies where post-LLM groups performed worse in syntax-based assessments. Educators can mitigate this by integrating LLMs into assignments that focus on problem-solving while requiring manual coding for foundational skills, ensuring balanced learning trajectories. Link to paper in comments.

I’ve been building and managing data systems at Amazon for the last 8 years. Now that AI is everywhere, the way we work as data engineers is changing fast. Here are 5 real ways I (and many in the industry) use LLMs to work smarter every day as a Senior Data Engineer: 1. Code Review and Refactoring LLMs help break down complex pull requests into simple summaries, making it easier to review changes across big codebases. They can also identify anti-patterns in PySpark, SQL, and Airflow code, helping you catch bugs or risky logic before it lands in prod. If you’re refactoring old code, LLMs can point out where your abstractions are weak or naming is inconsistent, so your codebase stays cleaner as it grows. 2. Debugging Data Pipelines When Spark jobs fail or SQL breaks in production, LLMs help translate ugly error logs into plain English. They can suggest troubleshooting steps or highlight what part of the pipeline to inspect next, helping you zero in on root causes faster. If you’re stuck on a recurring error, LLMs can propose code-level changes or optimizations you might have missed. 3. Documentation and Knowledge Sharing Turning notebooks, scripts, or undocumented DAGs into clear internal docs is much easier with LLMs. They can help structure your explanations, highlight the “why” behind key design choices, and make onboarding or handover notes quick to produce. Keeping platform wikis and technical documentation up to date becomes much less of a chore. 4. Data Modeling and Architecture Decisions When you’re designing schemas, deciding on partitioning, or picking between technologies (like Delta, Iceberg, or Hudi), LLMs can offer quick pros/cons, highlight trade-offs, and provide code samples. If you need to visualize a pipeline or architecture, LLMs can help you draft Mermaid or PlantUML diagrams for clearer communication with stakeholders. 5. Cross-Team Communication When collaborating with PMs, analytics, or infra teams, LLMs help you draft clear, focused updates, whether it’s a Slack message, an email, or a JIRA comment. They’re useful for summarizing complex issues, outlining next steps, or translating technical decisions into language that business partners understand. LLMs won’t replace data engineers, but they’re rapidly raising the bar for what you can deliver each week. Start by picking one recurring pain point in your workflow, then see how an LLM can speed it up. This is the new table stakes for staying sharp as a data engineer.

Large Language Models (LLMs) have quickly become the world's best interns and are accelerating toward becoming decent business analysts. A groundbreaking study by professors at the University of Chicago explores the potential of LLMs in financial statement analysis: • An LLM (GPT-4) outperformed human analysts in predicting earnings direction, achieving 60% accuracy vs 53% for analysts. • The LLM's predictions complement human analysts, excelling where humans struggled. This situation mirrors developments in medical imaging, where specific machine learning algorithms have shown superior performance to human radiologists in particular tasks, such as detecting lung nodules or classifying mammograms. Like in finance, these AI tools don't replace radiologists but complement their expertise • LLM performance was on par with specialized machine learning models explicitly trained for earnings prediction. • The LLM generated valuable narrative insights about company performance, not relying on memorized data. • Trading strategies based on LLM predictions yielded higher Sharpe ratios and alphas than other models. Beyond Financial Analysis, LLMs show promise in augmenting various areas of commercial analytics. For example, LLMS can process complex market dynamics, competitor actions, and transactional data to suggest optimal pricing strategies across product lines. Companies can leverage LLMs for rapid information synthesis (i.e., extracting critical points from large amounts of text/data), identifying anomalies, generating hypotheses, standardizing analyses, and personalized insights. Combined with Knowledge Graphs (LLMs + RAGs), they can be very powerful. Finance and other analytics professionals should explore integrating LLM-based analysis into their workflows. While LLMs show promise, human judgment remains crucial. Consider using LLMs to augment analysis, flag potential issues, and generate additional insights to enhance decision-making processes across finance, supply chain, marketing, and pricing strategies. As highlighted by Rob Saker, these findings underscore the potential for AI to revolutionize financial forecasting and business analytics more broadly. Every forward-thinking team should explore leveraging LLMs to enhance their analytical capabilities, decision-making processes, and operational efficiency. Please note, however, that while LLMs show great promise, they are not infallible, and this technology is still in the infant stages of "AI." They can produce convincing but incorrect information (hallucinations), may perpetuate biases present in their training data, and lack a true understanding of context. Human oversight, critical thinking, and domain expertise remain crucial in interpreting and applying LLM-generated insights. #revenue_growth_analytics #LLMs

LLMs are the single fastest way to make yourself indispensable and give your team a 30‑percent productivity lift. Here is the playbook. Build a personal use‑case portfolio Write down every recurring task you handle for clients or leaders: competitive intelligence searches, slide creation, meeting notes, spreadsheet error checks, first‑draft emails. Rank each task by time cost and by the impact of getting it right. Start automating the items that score high on both. Use a five‑part prompt template Role, goal, context, constraints, output format. Example: “You are a procurement analyst. Goal: draft a one‑page cost‑takeout plan. Context: we spend 2.7 million dollars on cloud services across three vendors. Constraint: plain language, one paragraph max. Output: executive‑ready paragraph followed by a five‑row table.” Break big work into a chain of steps Ask first for an outline, then for section drafts, then for a fact‑check. Steering at each checkpoint slashes hallucinations and keeps the job on‑track. Blend the model with your existing tools Paste the draft into Excel and let the model write formulas, then pivot. Drop a JSON answer straight into Power BI. Send the polished paragraph into PowerPoint. The goal is a finished asset, not just a wall of text. Feed the model your secret sauce Provide redacted samples of winning proposals, your slide master, and your company style guide. The model starts producing work that matches your tone and formatting in minutes. Measure the gain and tell the story Track minutes saved per task, revision cycles avoided, and client feedback. Show your manager that a former one‑hour job now takes fifteen minutes and needs one rewrite instead of three. Data beats anecdotes. Teach the team Run a ten‑minute demo in your weekly stand‑up. Share your best prompts in a Teams channel. Encourage colleagues to post successes and blockers. When the whole team levels up, you become known as the catalyst, not the cost‑cutting target. If every person on your team gained back one full day each week, what breakthrough innovation would you finally have the bandwidth to launch? What cost savings could you achieve? What additional market share could you gain?

Most people still think of LLMs as “just a model.” But if you’ve ever shipped one in production, you know it’s not that simple. Behind every performant LLM system, there’s a stack of decisions, about pretraining, fine-tuning, inference, evaluation, and application-specific tradeoffs. This diagram captures it well: LLMs aren’t one-dimensional. They’re systems. And each dimension introduces new failure points or optimization levers. Let’s break it down: 🧠 Pre-Training Start with modality. → Text-only models like LLaMA, UL2, PaLM have predictable inductive biases. → Multimodal ones like GPT-4, Gemini, and LaVIN introduce more complex token fusion, grounding challenges, and cross-modal alignment issues. Understanding the data diet matters just as much as parameter count. 🛠 Fine-Tuning This is where most teams underestimate complexity: → PEFT strategies like LoRA and Prefix Tuning help with parameter efficiency, but can behave differently under distribution shift. → Alignment techniques- RLHF, DPO, RAFT, aren’t interchangeable. They encode different human preference priors. → Quantization and pruning decisions will directly impact latency, memory usage, and downstream behavior. ⚡️ Efficiency Inference optimization is still underexplored. Techniques like dynamic prompt caching, paged attention, speculative decoding, and batch streaming make the difference between real-time and unusable. The infra layer is where GenAI products often break. 📏 Evaluation One benchmark doesn’t cut it. You need a full matrix: → NLG (summarization, completion), NLU (classification, reasoning), → alignment tests (honesty, helpfulness, safety), → dataset quality, and → cost breakdowns across training + inference + memory. Evaluation isn’t just a model task, it’s a systems-level concern. 🧾 Inference & Prompting Multi-turn prompts, CoT, ToT, ICL, all behave differently under different sampling strategies and context lengths. Prompting isn’t trivial anymore. It’s an orchestration layer in itself. Whether you’re building for legal, education, robotics, or finance, the “general-purpose” tag doesn’t hold. Every domain has its own retrieval, grounding, and reasoning constraints. ------- Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Can we finetune our LLM and retriever together to improve RAG performance? This paper proposes a technique to do exactly that! RAG Basics: When you prompt an LLM, RAG supplies relevant documents. A separate retrieval model computes the probability of each text chunk being relevant and provides the top chunks to the LLM. The LLM generates tokens based on the chunks, prompt, and previous tokens. In Short: Fine-tuning LLMs and retrieval models together improves performance without extensive data processing, enabling better retrieval-augmented generation. LLMs aren't exposed to retrieval-augmented inputs during pretraining, limiting their ability to use retrieved text effectively. Fine-tuning the LLM and retrieval model together can improve performance without requiring extensive data processing. How it Works: Authors from Meta fine-tuned Llama 2 (65B parameters) and DRAGON+, a retriever, to create RA-DIT 65B. They fine-tuned Llama 2 on prompts with retrieved text and questions, and fine-tuned DRAGON+ to retrieve more relevant chunks. Fine-tuning was supervised for tasks like question-answering and self-supervised for text chunk completion. Results: RA-DIT 65B achieved 49.1% accuracy on average across four question datasets, outperforming LLaMA 2 65B with DRAGON+ (45.1%) and LLaMA 2 65B alone (32.9%). With five example inputs, RA-DIT 65B reached 51.8% accuracy. RA-DIT offers an efficient way to enhance LLM performance with RAG, making it a valuable technique for developers. Details: RA-DIT fine-tunes Llama 2 and DRAGON+ to work together effectively, leveraging the strengths of both models to generate better output. By fine-tuning the LLM to better use retrieved knowledge and the retrieval model to select more relevant text, RA-DIT achieves improved performance without requiring extensive data processing. https://lnkd.in/gf4fGVkC

Groundbreaking Research Alert: Making LLMs More Efficient with Smart Retrieval A fascinating paper from NAVER LABS Europe introduces a novel approach to optimize Large Language Models' retrieval mechanisms. The research shows how we can reduce retrieval operations by over 50% while maintaining or even improving performance. Key Technical Insights: - The system uses an "I Know" (IK) classifier that achieves 80% accuracy in determining when an LLM needs external knowledge - Only 32 tokens from the initial response are needed to make this determination - Training requires just 20,000 samples to achieve optimal performance - The approach works across multiple model families including Mistral, Llama, Gemma, and SOLAR Under the hood: - The system employs an LLM-as-judge architecture for training data generation - It uses adapters for fine-tuning larger models (7B+) - The IK score is computed using softmax on Yes/No token logits - Processing time is remarkably efficient: 3.7ms for IK classification, 8.3ms for generating 32 tokens Real-world Impact: - Reduces RAG processing time by up to 80% - Improves efficiency across various datasets including NQ, ASQA, HotpotQA - Particularly effective for general knowledge datasets like TriviaQA and SCIQ This research represents a significant step forward in making LLMs more efficient and practical for real-world applications. The ability to selectively activate retrieval mechanisms could be a game-changer for deployment at scale.

There’s been a lot of discussion about how Large Language Models (LLMs) power customer-facing features like chatbots. But their impact goes beyond that—LLMs can also enhance the backend of machine learning systems in significant ways. In this tech blog, Coupang’s machine learning engineers share how the team leverages LLMs to advance existing ML products. They first categorized Coupang’s ML models into three key areas: recommendation models that personalize shopping experiences and optimize recommendation surfaces, content understanding models that enhance product, customer, and merchant representation to improve shopping interactions, and forecasting models that support pricing, logistics, and delivery operations. With these existing ML models in place, the team integrates LLMs and multimodal models to develop Foundation Models, which can handle multiple tasks rather than being trained for specific use cases. These models improve customer experience in several ways. Vision-language models enhance product embeddings by jointly modeling image and text data; weak labels generated by LLMs serve as weak supervision signals to train other models. Additionally, LLMs also enable a deeper understanding of product data, including titles, descriptions, reviews, and seller information, resulting in a single LLM-powered categorizer that classifies all product categories with greater precision. The blog also dives into best practices for integrating LLMs, covering technical challenges, development patterns, and optimization strategies. For those looking to elevate ML performance with LLMs, this serves as a valuable reference. #MachineLearning #DataScience #LLM #LargeLanguageModel #AI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gvaUuF4G

LLMs “think harder” in a latent space? New paper demonstrates that by allowing LLMs to iterate in its latent space (like "thinking" multiple times about the same input) improves performance comparable to much larger models. Implementation (simplified): 1️⃣ 3-part architecture: 1. Prelude: Transforms input tokens into latent space; Recurrent Block: Core "thinking" component that iterates multiple times; Coda: Converts final latent state to output tokens 2️⃣ Train with randomized recurrence steps (log-normal Poisson sampling) and truncated backpropagation through last 8 iterations 3️⃣ Deploy with dynamic recurrence steps (4-64) at inference for compute scaling with KV-cache sharing and KL-based early stopping for efficiency. Insights: 📈 Achieves 34.8% strict accuracy on GSM8K (5x baseline) with 32 recurrence 🛠️ "Sandwich" normalization and input reinjection prevent hidden state collapse 📚 Performs best on code/math tasks (23% HumanEval) with data mix containing 31.5% STEM content 🔄 Shows latent space "reasoning orbits" that correlate with task difficulty ⚡ KV-cache sharing reduces memory usage by 75% during long reasoning chains 📚 Performance gains vary by task; easier tasks saturate with fewer iterations, while harder tasks benefit from more. Paper: https://lnkd.in/e_iRk4Xd