Beyond the Hype: A Realistic Look at Large Language Models • Jodie Burchell • GOTO 2024

Jody Burchill, developer advocate at JetBrains speech midway 2024 A realistic look at large language models (LLM)development, opportunities and limitations. She sought to dispel the hype around LLMs and present a balanced understanding of their applications.

1. History and development of LLM LLMs are part of a long history of natural language processing research (NLP)Starting with automating text tasks, such as classifying and generalizing texts Advances in technology, such as CUDA (for training neural networks on graphics processors) and access to large data sets (For example, Common Crawl)It allowed us to create more powerful models. An important step was the network with LSTM (Long short-term memory)These were developed in the mid-2000s that improved context processing, but their limitations led to the creation of transformers. 2. Transformers and GPT models Transformers have replaced sequential text processing with parallel, making them more efficient for NLP. Generative pre-trained transformers (GPT) They are the basis of modern models.

• Since GPT was released.1 before GPT--4 There is a significant improvement in the quality of text generation: -- GPT-1 Create grammatically correct text without context. -- GPT-3 He began to take context into account and encode language information. -- GPT-4 reach 1 It's a trillion parameters and it's become more universal. 3. Opportunities and Limitations of LLM LLMs demonstrate impressive results in NLP tasks such as translation, text summation, and answering questions. However, they remain limited: Models can remember training data instead of generalizing. Their ability to solve new problems depends on the level of generalization: Local generalization: working with similar examples. Broad generalization: solving problems in different areas. Extreme generalization: going beyond human capabilities (unattainable for current models). The problem of assessing the intelligence of models is related to the focus on skills, not basic abilities. 4. Application and case studies Jody demonstrates using LLM to extract information from databases using RAG approach (exploratory): Separating documents into parts, creating attachments and saving them in a vector database. Convert queries into attachments to search for relevant data. Create applications to answer questions using tools like LangChain. Example: Search information in PyCharm documentation using the ChatGPT model. About RAG separately interesting to read the articleEmerging Patterns in Building GenAI Products"that I mean." told earlier 5. Performance assessment and model selection The effectiveness of models depends on the subject area and use case. For complex tasks, it may be necessary to set up a model or create specialized data sets, and leaderboards such as: HuggingfaceIt helps to compare the performance of models. 6. Limitations and prospects Despite the successes, LLMs have not reached the level of artificial general intelligence. (AGI). They require careful tuning, task selection, and performance evaluation. Jodie stressed the importance of a realistic approach to LLM expectations, comparing current challenges to those that have existed in software development and machine learning for decades.

Thus, Jody Burchill’s speech emphasized the need for a sober assessment of LLM’s capabilities, limitations and practical applications.

#AI #ML #Engineering #Software #Architecture #SystemDesign #DistributedSystems