Techniques To Enhance AI Performance With Minimal Data

An explanation of language model distillation, how it works, why it’s useful, and examples of how you can perform distillation. What is distillation? Distillation is a model compression technique where a smaller "student" model is trained to mimic the behavior of a larger "teacher" model. This is achieved by transferring knowledge from the teacher to the student, usually through methods like logit-based or hidden states-based distillation. These methods are designed to help the student model replicate the teacher's output distribution or internal representations, often leading to a more efficient model with comparable performance. When would we use this? Distillation is commonly used when deploying large models is impractical due to resource constraints, such as in real-time applications or edge devices. For instance, a smaller student model can be distilled from a powerful teacher model like Llama3.1 405B, retaining much of the original model’s capability but with significantly lower computational demands. Distillation is also useful when adapting models to specific tasks or domains, as seen in domain-specific distillation cases like "function calling," where specialized knowledge from a teacher model is transferred to a smaller model for specific use cases. What’s the benefit? Distillation offers a significant reduction in model size and computational requirements while maintaining a high level of performance. This is especially valuable in scenarios where memory and processing power are limited. Moreover, distillation allows for flexibility in model architecture choices; for example, distilling knowledge from a Llama-3.1-70B model into a much smaller StableLM-2-1.6B model. Distillation methods like those provided in Arcee-AI's DistillKit, including logit-based and hidden states-based distillation, can lead to substantial performance gains over traditional training routines without requiring additional data. Examples of Distillation Techniques: (1) Logit-based Distillation: This method involves transferring knowledge by using both the hard targets (actual labels) and soft targets (teacher logits) to guide the student model. The student is trained to minimize the difference between its output distribution and the teacher’s output, typically using Kullback-Leibler (KL) divergence. This method is particularly effective for maintaining performance close to the teacher model while improving the student’s generalization abilities. (2) Hidden States-based Distillation:  Here, the focus is on aligning the intermediate layer representations of the student with those of the teacher. This layer-wise guidance helps the student model capture similar features and improves its performance and generalization. This method also allows for cross-architecture distillation, enabling knowledge transfer between different model architectures, such as distilling from a Llama-3.1-70B model into a StableLM-2-1.6B model.

Few-shot prompting is great, but dynamic few-shot prompting? Even better. Franklin Lindemberg's latest prompting technique leverages OpenAI embedding to optimize few-shot learning by using a dynamic few-shot prompt technique. Instead of bombarding the model with too many examples, this method dynamically selects only the most relevant ones, ensuring efficiency without sacrificing quality. How it works: (1) A vector store holds a library of input-output examples (2) An Embedding model transforms user input into vectors for querying the store (3) An LLM completes the tasks based on the most relevant examples. Following this approach, only the top 3 examples are pulled, leading to faster and cheaper generation thanks to fewer tokens being used. Also, selecting the most relevant examples leads to better performance. This approach refines the few-shot technique into a more scalable and cost-effective method for use cases like displaying data in tables, classifying text, or summarizing documents. Full blog and code examples post https://lnkd.in/gwGr2DTa — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai

The whole LLM community is underestimating the power of auto-prompt optimization, especially in academics. The effectiveness of prompt engineering (in-context learning) even caught Dr. Manning by surprise. But it is what has made LLMs as prevalent as they are right now. Model fine-tuning with methods such as SFT and DPO (Direct Preference Optimization) is researched far more than auto-prompt optimization and is considered much cooler in the research world. It is true that model fine-tuning is crucial for democratizing LLMs, enabling their adaptation to various end use cases with an OS model without solely relying on proprietary providers. But a huge missing piece is: where does the training dataset come from? In academics, most researchers don’t care about this, as they can use publicly available datasets. But for product teams, you have to make your own datasets. So how can prompt engineering help? Assume we start with a best model (teacher) and an OS (student) you want to optimize. [Teacher and student can also be the same model.] 1️⃣ Leveraging one of the best models, plus a small golden validation and training dataset manually labeled, you can create the training datasets for SFT. [You maximize the performance of existing models and create maybe a 90% accurate training dataset.] 2️⃣ Leveraging an aligned LLM judge, you can create a preference dataset using the student and the teacher. With Step 1 and Step 2, you will optimize your student model to its maximum with minimum human-labeled data. Ideally, you should do this iteratively. But the bottleneck is manual prompt engineering. Every time you fine-tune your target model, you need to go through the manual engineering. Sometimes your app pipeline is complicated, and manual prompting is not even feasible. That is the beauty of auto-prompt optimization for any LLM task pipeline. It closes the loop of optimization with minimal human labeling, relying only on the starter validation and a small training dataset, as in-context learning is essentially the most effective few-shot learning. AdalFlow is a greatly undermined library, but as we mature and combine that with model fine-tuning, the world will be shocked by its power. #artificialintelligence #machinelearning #llms #adalflow