Introduction
Large Language Models (LLMs) have become a cornerstone in the field of artificial intelligence, offering valuable assistance in various tasks. However, they sometimes face challenges, such as inconsistent accuracy and a lack of real – time knowledge updates. This article, inspired by a talk by Ayush Thakur at the DataHack Summit 2024, explores how future technologies like function calling and Retrieval – Augmented Generation (RAG) can enhance LLMs.
What are LLMs?
LLMs are sophisticated AI systems that analyze large datasets to understand and generate natural language. Models like GPT – 4 and LLaMA use deep – learning algorithms to handle tasks like language translation and content creation by learning language patterns from vast amounts of data.
Limitations of LLMs
LLMs have several limitations. Their accuracy can be inconsistent, especially in complex situations. They may lack true comprehension, producing text that seems reasonable but is incorrect. Their outputs are also constrained by training data, which can be biased or incomplete, and they have a static knowledge base that doesn’t update in real – time.
Importance of Structured Outputs for LLMs
Structured outputs are crucial for LLMs. They enhance consistency, improve usability by making information easier to interpret, help organize data logically, and reduce ambiguity in the generated text.
Interacting with LLM: Prompting
Prompting LLMs involves creating a prompt with instructions, context, input data, and an output indicator. There are different prompting approaches, such as input – output, Chain of Thought (CoT), and Self – Consistency with CoT (CoT – SC), which help refine LLM responses.
How does LLM Application differ from Model Development?
Model development focuses on architecture, datasets, and long – running optimizations, while LLM apps are about the composition of functions, APIs, and config, with human – generated and often unlabeled datasets and high – frequency interactions.
Function Calling with LLMs
Function calling allows LLMs to execute predefined functions during response generation, expanding their capabilities. It has benefits like enhanced interactivity, increased versatility, improved accuracy, and streamlined processes. However, current LLMs face limitations in integration, security, execution, and management.
Function Calling Meets Pydantic
Pydantic objects simplify the schema – definition and conversion process for function calling, offering automatic schema conversion, enhanced code quality, robust error handling, and framework integration.
Function Calling: Fine – tuning
Fine – tuning small LLMs for niche tasks can enhance function calling. This involves data curation, single – turn and parallel calls, nested calls, multi – turn chat, using special tokens, and LoRA fine – tuning.
RAG (Retrieval – Augmented Generation) for LLMs
RAG combines retrieval and generation to improve LLMs. It works through components like a document loader, chunking strategy, embedding model, retriever, node parsers & postprocessing, response synthesizer, and evaluation. It offers benefits such as improved accuracy, enhanced contextual relevance, increased knowledge coverage, better handling of long – tail queries, and an enhanced user experience.
Evaluation of LLMs
Evaluating LLMs is essential for ensuring accuracy, guiding improvements, measuring against benchmarks, ensuring ethical use, and supporting real – world applications. However, it faces challenges like subjectivity in metrics, difficulty in measuring nuanced understanding, scalability issues, bias and fairness concerns, and the dynamic nature of language.
Constrained Generation of Outputs for LLMs
Constrained generation ensures that LLMs produce outputs that adhere to specific constraints, which is important in applications like legal documentation.
Lowering Temperature for More Structured Outputs
Lowering the temperature parameter in LLMs reduces randomness, resulting in more structured and predictable outputs, which is beneficial for applications requiring consistency.
Chain of Thought Reasoning for LLMs
Chain of thought reasoning helps LLMs generate more comprehensive responses by following a logical sequence of steps, similar to human reasoning.
Function Calling on OpenAI vs Llama
OpenAI and Llama models have different function – calling capabilities. Understanding these differences is crucial for choosing the right model for applications with complex external interactions.
Finding LLMs for Your Application
Selecting the right LLM requires assessing its capabilities, scalability, and alignment with specific data and integration needs. Referencing performance benchmarks and platforms like the LMSYS Chatbot Arena Leaderboard can be helpful.
Conclusion
LLMs are evolving with the help of function calling and RAG, which add structured outputs and real – time data retrieval. Despite their potential, their limitations call for further refinement. Techniques like constrained generation, lowering temperature, and chain of thought reasoning can enhance their reliability and relevance. Understanding the differences between OpenAI and Llama models’ function – calling abilities is key to optimizing their use in various applications.
