Summary of A Survey of Techniques for Maximizing LLM Performance

• 00:00:00

  In this breakout session at OpenAI's developer conference, John Allard, an engineering lead, introduces himself and discusses the techniques for maximizing LLM (Language Model) performance. He highlights the success of OpenAI's fine-tuning product and the collaboration with developers from various industries to solve problems using LLMs. Colin, from OpenAI's solutions practice, emphasizes the challenges of optimizing LLMs and the need for a framework and tools to address them. The session aims to provide insights into identifying and solving optimization issues with LLMs.

• 00:02:31

  Today's talk focuses on maximizing performance in LLMs. It is important to understand the different options available and when to use them. Instead of following a linear approach, it is better to optimize on two axes: context optimization and LLM optimization. Prompt engineering is a good starting point and allows for quick testing and learning. From there, you can determine if you need more context (retrieval-augmented generation) or more consistent instruction following (fine-tuning). Sometimes, both approaches are necessary. The optimization journey typically involves starting with a prompt, creating an evaluation, establishing a baseline, and then adding few-shot examples and retrieval augmentation.

• 00:05:04

  The process of optimization in maximizing LLM performance involves trying different techniques, evaluating their effectiveness, and making adjustments accordingly. This includes fine-tuning the model, optimizing retrieval-augmented generation, and incorporating prompt engineering strategies. Prompt engineering involves writing clear instructions, breaking down complex tasks into simpler subtasks, allowing the model time to think, and systematically testing changes. The next step is often extending the model's capabilities with reference text or external tools, which falls under retrieval-augmented generation.

• 00:07:32

  Prompt engineering is a valuable technique for optimizing LLM performance, particularly in the early stages of testing and learning. It provides a baseline for further optimization and evaluation. However, prompt engineering is limited in its ability to introduce new information, replicate complex styles or methods, and minimize token usage. To improve prompts, clear instructions, allowing time for thinking, and breaking down complex tasks into simpler tasks are effective strategies. Overall, prompt engineering is a helpful starting point, but it may not be sufficient for all use cases.

• 00:09:57

  One approach to maximizing the model's performance is to treat it as a show rather than tell problem. By providing a few-shot examples of the desired behavior, the model can learn and improve its performance. This can lead to good performance improvement in practical tasks. To further enhance the contextual relevance of the examples, retrieval-augmented generation (RAG) is commonly employed. RAG involves providing the model with access to domain-specific content, such as a knowledge base, to improve its ability to answer questions accurately. RAG can be likened to giving the model an open book during an exam, where it can retrieve the necessary information based on the methodology it has learned. Without the context and content, certain problems may be impossible to solve.

• 00:12:19

  In this survey, the authors discuss the use of retrieval-augmented generation (RAG) for improving language model performance. They explain how RAG works by incorporating contextual knowledge from a knowledge base to answer specific questions. RAG is particularly useful for introducing new information to the model and reducing hallucinations by controlling content. However, it is not effective for embedding understanding of a broad domain or teaching the model a new language format or style. The authors suggest combining prompt engineering with RAG to achieve desired accuracy levels.

• 00:14:47

  The speaker discusses their experience with optimizing the performance of an LLM (language model) by using techniques like prompt engineering and RAG (Retrieval-Augmented Generation). They share a success story where they improved the accuracy of a customer's RAG pipeline from 45% to 65% through various iterations, including trying hypothetical document embeddings, fine-tuning the embedding space, and chunking and embedding different parts of content. They also mention the use of re-ranking and classification techniques to further improve performance. Despite the challenges and multiple iterations, they persevered and achieved significant performance improvements.

• 00:17:08

  In this section, the speaker discusses different techniques they used to improve the performance of their language model. They experimented with prompt engineering, introducing tools for structured data questions, query expansion, and synthesis. These techniques helped them achieve 98% accuracy without the need for fine-tuning. However, the speaker also mentions a cautionary tale where retrieval augmented generation (RAG) backfired in a customer example, resulting in a humorous yet erroneous response.

• 00:19:39

  The content and search quality are crucial for the model to give correct answers. RAG introduces additional factors that can lead to incorrect results. Exploding Gradients has developed a framework called Ragas that evaluates the performance of the model. It measures two metrics for the LLM side - faithfulness, which checks whether the answer aligns with the content, and answer relevancy, which assesses if the model addresses the original query. On the content side, the framework evaluates the relevance of the information provided to the question. Adding excessive context can lead to incorrect results and hallucinations. Thus, the metric focuses on the precision of relevant content.

• 00:22:02

  The speaker discusses three metrics for maximizing LLM performance. The first metric is context precision, which measures the amount of content that is actually used in answering a question. It suggests determining if adding more context improves accuracy. The second metric is context recall, which evaluates if all relevant information is retrieved for answering a question. If recall is low, search optimization, reranking, or different embeddings may be necessary. The speaker also mentions that sometimes the issue lies in the task itself, requiring fine-tuning instead of prompt engineering. Fine-tuning involves training a model on a smaller, more specific dataset to create a different model altogether.

• 00:24:40

  Fine-tuning is a technique where general models are specialized for specific tasks. The two primary benefits of fine-tuning are: 1) achieving performance levels that would be impossible without fine-tuning, as fine-tuning allows for more data to be shown to the model compared to using prompting techniques, and 2) fine-tuned models are often more efficient to interact with, requiring fewer prompt tokens and resulting in quicker and more cost-efficient responses. Fine-tuning can also involve distilling knowledge from a larger model to a smaller one, which is more efficient in terms of cost and latency. An example task that can be solved using fine-tuning is extracting structured information from a natural language description of a real estate listing, which would require complex instructions and in-context examples without fine-tuning.

• 00:27:09

  The speaker discusses a mistake in the output where the date was templated to be the current date instead of the desired date. They propose adding a new rule or in-context example to fix this issue using fine-tuning. Fine-tuning involves starting with a simple dataset and training a model to improve its performance. They highlight that fine-tuning works well for emphasizing existing knowledge in the base model and modifying the structure or tone of the model's output. However, it is not effective for adding new knowledge to the model.

• 00:29:36

  Large pre-training runs are not ideal for incorporating new knowledge, so techniques like RAG would be more suitable. Fine-tuning is a slower process for iterating on new use cases and requires significant investment in creating datasets and components. Canva used fine-tuning with the 3.5 Turbo model to improve the performance of generating design mocks. Expert evaluators found that the fine-tuned model outperformed both the base model and GPT-4. This success was attributed to the fact that no new knowledge was required, specific output structures were needed, high-quality training data was used, and baselines were established for comparison.

• 00:32:04

  Fine-tuning was considered a good approach for the task at hand. However, a cautionary tale is shared about a writer who wanted an AI assistant to replicate their writing tone but found that the base models failed to do so. They attempted to fine-tune a model using two years' worth of Slack messages but ended up with a model that replicated the terse and informal style of communication on Slack instead. The mistake made was not fully considering whether the provided data accurately represented the desired end behavior. It would have been better to experiment with a smaller set of messages first before fine-tuning on different types of content such as emails or blog posts.

• 00:34:41

  To fine-tune a model, you first need a dataset, which can be obtained through various methods such as downloading open-source data, buying data, or collecting and labeling data yourself. Once you have the dataset, you can start the training process, which depends on the method you choose. It's important to understand the hyperparameters and their impact on the model. The loss function is crucial in fine-tuning language models (LLMs), but it may not always correlate with performance on the desired tasks. Evaluating the model can be done through human ranking or comparing outputs of different models. Finally, deploying the model and sampling from it at inference time completes the process, forming a feedback loop with the previous steps.

• 00:37:03

  To maximize LLM performance, the following techniques are recommended: 1. Start with prompt engineering and few-shot learning to gain insight into how LLMs work on your problem. 2. Establish a baseline before fine-tuning by experimenting with different models and understanding their strengths and weaknesses. 3. Begin fine-tuning with a small, high-quality dataset and evaluate the model's performance. 4. Use an active learning approach to identify areas where the model struggles and target those areas with new data. 5. Focus on data quality rather than quantity during fine-tuning, as the quantity aspect is already addressed during pre-training. 6. Consider combining fine-tuning with RAG (Retrieval-Augmented Generation) for certain use cases. This allows the model to understand complex instructions during fine-tuning and reduces the need for complex prompts at sample time. 7. Be cautious not to oversaturate the context with irrelevant information that may have spurious correlations to the problem at hand.

• 00:39:25

  The speaker discusses the application of the techniques mentioned earlier in the talk to solve a specific problem: generating syntactically correct SQL queries based on a given natural language question and a database schema. They start with a simple retrieval approach, using cosine similarity to find similar SQL queries. However, they find that this method is not effective because the answer to a question can vary depending on the database schema. So, they switch to using hypothetical document embedding and generating a hypothetical SQL query to perform similarity search. This approach significantly improves their results. They also try contextual retrieval by filtering based on question hardness, which leads to further improvements. Finally, they settle on a self-consistency check method, where the system builds and runs a query, identifies errors, and allows the model to fix itself. This approach proves to work well.

• 00:42:04

  In a use case where latency and cost are not major concerns, various techniques were applied to improve LLM performance. Prompt engineering initially resulted in a 69% performance. Few-shot examples and RAG techniques further improved performance by a few points. Using hypothetical question and answer embeddings boosted performance by 3% and 5% respectively. Simply increasing the number of examples achieved a performance close to the state of the art. Fine-tuning with partners increased performance to 82% with prompt engineering and up to 83.5% with basic RAG techniques. Notably, these techniques yielded results without the need for complex data pre-processing or post-processing. This highlights the effectiveness of simple fine-tuning and prompt engineering in achieving state-of-the-art performance on a well-known benchmark. In summary, prompt engineering and fine-tuning, along with RAG techniques, can significantly enhance LLM performance with relatively low investment and quick iterations.

• 00:44:36

  Iterating on the prompt is a viable technique to maximize performance in language model models. Once a performance plateau is reached, error analysis is needed. If new knowledge or context is required, RAG is recommended. If the model struggles with following instructions or needs to adhere to a strict or unique output structure, fine-tuning can be tried. It's important to note that the process is non-linear, with multiple iterations and switching between techniques.