Testing Methods for Scaling LLM Performance

🚀 New Blog Alert: Exploring Test-Time Compute (TTC) for LLMs 🧠 Everyone's talking about Test-Time Computation (TTC) as a transformative way to improve LLM performance. But what is TTC really about, and why does it matter now? In this blog post, I highlight some key aspects of TTC, including strategies like adaptive distribution updates, self-verification, and Monte Carlo Tree Search (MCTS). These advanced techniques enable LLMs to refine their outputs dynamically at inference, unlocking better quality and efficiency. 🔍 Why TTC Matters Now: Performance: On the challenging MATH dataset, TTC improved test accuracy by up to 21.6% without retraining (Snell et al., 2024). Efficiency: Compute-optimal strategies have demonstrated over 4x efficiency gains compared to traditional methods. Scalability Alternative: Smaller models enhanced with TTC outperformed larger models lacking it, showing that size isn't everything. Future AI Paradigm: TTC challenges the "bigger is better" model of AI development, pointing toward more adaptable and resource-efficient systems. The blog also includes a Python example integrating LLaMA-3 with MCTS for reasoning tasks—perfect for those eager to experiment with TTC in their projects. #AI #MachineLearning #LargeLanguageModels #TTC #TechInnovation #AIEngineering

In the last three months alone, over ten papers outlining novel prompting techniques were published, boosting LLMs’ performance by a substantial margin. Two weeks ago, a groundbreaking paper from Microsoft demonstrated how a well-prompted GPT-4 outperforms Google’s Med-PaLM 2, a specialized medical model, solely through sophisticated prompting techniques. Yet, while our X and LinkedIn feeds buzz with ‘secret prompting tips’, a definitive, research-backed guide aggregating these advanced prompting strategies is hard to come by. This gap prevents LLM developers and everyday users from harnessing these novel frameworks to enhance performance and achieve more accurate results. https://lnkd.in/g7_6eP6y In this AI Tidbits Deep Dive, I outline six of the best and recent prompting methods: (1) EmotionPrompt - inspired by human psychology, this method utilizes emotional stimuli in prompts to gain performance enhancements (2) Optimization by PROmpting (OPRO) - a DeepMind innovation that refines prompts automatically, surpassing human-crafted ones. This paper discovered the “Take a deep breath” instruction that improved LLMs’ performance by 9%. (3) Chain-of-Verification (CoVe) - Meta's novel four-step prompting process that drastically reduces hallucinations and improves factual accuracy (4) System 2 Attention (S2A) - also from Meta, a prompting method that filters out irrelevant details prior to querying the LLM (5) Step-Back Prompting - encouraging LLMs to abstract queries for enhanced reasoning (6) Rephrase and Respond (RaR) - UCLA's method that lets LLMs rephrase queries for better comprehension and response accuracy Understanding the spectrum of available prompting strategies and how to apply them in your app can mean the difference between a production-ready app and a nascent project with untapped potential. Full blog post https://lnkd.in/g7_6eP6y

Prompt formatting can have a dramatic impact on LLM performance, but it varies substantially across models. Some pragmatic findings from a recent research paper: 💡 Prompt Format Significantly Affects LLM Performance. Different prompt formats (plain text, Markdown, YAML, JSON) can result in performance variations of up to 40%, depending on the task and model. For instance, GPT-3.5-turbo showed a dramatic performance shift between Markdown and JSON in code translation tasks, while GPT-4 exhibited greater stability. This indicates the importance of testing and optimizing prompts for specific tasks and models. 🛠️ Tailor Formats to Task and Model. Prompt formats like JSON, Markdown, YAML, and plain text yield different performance outcomes across tasks. For instance, GPT-3.5-turbo performed 40% better in JSON for code tasks, while GPT-4 preferred Markdown for reasoning tasks. Test multiple formats early in your process to identify which structure maximizes results for your specific task and model. 📋 Keep Instructions and Context Explicit. Include clear task instructions, persona descriptions, and examples in your prompts. For example, specifying roles (“You are a Python coder”) and output style (“Respond in JSON”) improves model understanding. Consistency in how you frame the task across different formats minimizes confusion and enhances reliability. 📊 Choose Format Based on Data Complexity. For simple tasks, plain text or Markdown often suffices. For structured outputs like programming or translations, formats such as JSON or YAML may perform better. Align the prompt format with the complexity of the expected response to leverage the model’s capabilities fully. 🔄 Iterate and Validate Performance. Run tests with variations in prompt structure to measure impact. Tools like Coefficient of Mean Deviation (CMD) or Intersection-over-Union (IoU) can help quantify performance differences. Start with benchmarks like MMLU or HumanEval to validate consistency and accuracy before deploying at scale. 🚀 Leverage Larger Models for Stability. If working with sensitive tasks requiring consistent outputs, opt for larger models like GPT-4, which show better robustness to format changes. For instance, GPT-4 maintained higher performance consistency across benchmarks compared to GPT-3.5. Link to paper in comments.

My favorite paper from NeurIPS’24 shows us that frontier LLMs don’t pay very close attention to their context windows… Needle In A Haystack: The needle in a haystack test is the most common way to test LLMs with long context windows. The test is conducted via the following steps: 1. Place a fact / statement within a corpus of text. 2. Ask the LLM to generate the fact given the corpus as input. 3. Repeat this test while increasing the size of the corpus and placing the fact at different locations. From this test, we see if an LLM “pays attention” to different regions of a long context window, but this test purely examines whether the LLM is able to recall information from its context. Where does this fall short? Most tasks being solved by LLMs require more than information recall. The LLM may need to perform inference, manipulate knowledge, or reason in order to solve a task. With this in mind, we might wonder if we could generalize the needle in a haystack test to analyze more complex LLM capabilities under different context lengths. BABILong generalizes the needle in a haystack test to perform long context reasoning. The LLM is tested based upon its ability to reason over facts that are distributed in very long text corpora. Reasoning tasks that are tested include fact chaining, induction, deduction, counting, list / set comprehension, and more. Such reasoning tasks are challenging, especially when necessary information is scattered in a large context window. “Our evaluations show that popular LLMs effectively utilize only 10-20% of the context and their performance declines sharply with increased reasoning complexity.” - BABILong paper Can LLMs reason over long context? We see in the BABILong paper that most frontier LLMs struggle to solve long context reasoning problems. Even top LLMs like GPT-4 and Gemini-1.5 seem to consistently use only ~20% of their context window. In fact, most LLMS struggle to answer questions about facts in texts longer than 10,000 tokens! What can we do about this? First, we should just be aware of this finding! Be wary of using super long contexts, as they might deteriorate the LLM’s ability to solve more complex problems that require reasoning. However, we see in the BABILong paper that these issues can be mitigated with a few different approaches: - Using RAG is helpful. However, this approach only works up to a certain context length and has limitations (e.g., struggles to solve problems where the order of facts matters). - Recurrent transformers can answer questions about facts from very long contexts.

How we implemented test-time computing for open models to solve complex math problems like OpenAI o1. 👀 Test-time compute methods use dynamic inference strategies to have LLMs “think longer” on harder problems, e.g. difficult math problems. By scaling test-time compute, smaller models can match or even surpass the performance of larger models. Meta Llama 3.2 3B can outperform Llama 3.1 70B on MATH-500!🤯 TL;DR: 🔍 Test-time compute scaling offers an alternative to training larger models by allowing smaller models to "think longer" 🎯 Explored Best-of-N sampling, beam search, and Diverse Verifier Tree Search (DVTS) 🦙 Llama 3 1B parameter model achieved 55% accuracy on the MATH benchmark using optimal search strategies 🧮 Process Reward Models (PRMs) played a crucial role in the search process by evaluating intermediate solution steps 📊 Different search strategies work better for different problem difficulties - beam search for harder problems, Best-of-N for simpler ones 🔄 Introduce DVTS, a new method of performance on larger compute budgets by maintaining solution diversity 💪 Using compute-optimal scaling, a Llama 3 3B outperforms 70B (22x larger) on mathematical reasoning tasks 🤗 Code and methods open source in a new library ,“learn and search” Blog: https://lnkd.in/egw28JQc Learn and Search Repo: https://lnkd.in/edSViQGK

Over the past year, we have been working on vertically scaling LLMs by combining caching with denoising models and segmentation of tasks into simpler, independent parts. This has enabled us to reduce the average number of queries to an LLM by 3-5 orders of magnitude and cost per datapoint by 2-3 orders of magnitude, without impacting accuracy, even on the hardest instances of a task. How are such efficiency gains possible? Let's make a few observations: - real-world tasks are often composed of multiple independent subtasks. For example, extracting key fields from invoices, medical records, legal proceedings, answering customer support messages containing multiple questions, assessing mechanical damage from images for insurance claims, and many more. - real-world subtasks are often over-defined. i.e. there is redundant information present in the input which does not meaningfully affect the output of the model. - what matters is the average cost per task, not the cost of every single task instance. - real-world throughputs are large. equivalent subtasks occur frequently in production. More details in the comments. Proud of Naré Vardanyan and the Ntropy team for making this happen. We're just getting started 🔥