![Summary
Beyond an all-out retraining, there are two
main techniques to improve LLM output
relevance summarized as [ref]:
● Fine-tune model for form (e.g., LoRA)
● Prompt expansion for fact (e.g., RAG)
Also, more LLMs are providing tools for
custom data (see demo: NotebookLM)
14
Source](https://image.slidesharecdn.com/customizingllms-231103202846-426ebdf1/85/Customizing-LLMs-14-320.jpg)
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Customizing LLMs
- 1. Customizing LLMs TechFrontiers AI Meetup, Nov 2, 2023 Jim Steele
- 2. Customizing Large Language Models LLMs allow humans to efficiently interact and generate content with basically “all” annotated publicly available human knowledge. But what about incorporating proprietary, domain specific, or private data into these models? 2 Custom knowledge ? Corpus of Human Knowledge (“The Internet”)
- 3. Three custom options for LLMs 3 Custom knowledge Corpus of Human Knowledge (“The Internet”) Train a custom LLM Tune general purpose LLM Prompt general purpose LLM
- 4. Custom options for LLMs (1 of 3) 4 Corpus of Human Knowledge (“The Internet”) Train a custom LLM Tune general purpose LLM Prompt general purpose LLM Training requires immense resources only available to the few such as Open AI, Google, and Meta. (Named “Foundation Models”) Closed models: ChatGPT/GPT-4 (Open AI), Bard/PaLM2 (Google), … Open models: LLaMA 2 (Meta), Claude 2 (Anthropic), …
- 5. Custom options for LLMs (2 of 3) 5 Corpus of Human Knowledge (“The Internet”) Train a custom LLM Tune general purpose LLM Prompt general purpose LLM Fine-tuning can be done post-training to incorporate or emphasize custom knowledge. These techniques are used in foundation models already!
- 6. Fine-tuning for LLMs (1 of 2) Prompt: “Write an essay about Alexander Hamilton.” LLM: “Your essay should be at least five pages, double-spaced, and include at least two citations.” source 6 Supervised Fine-Tuning (SFT): correct gross errors to be more in line with expected use-cases. Prioritization of quality examples over quantity, as numerous reports show that the use of high-quality data results in improved final model performance. Without tuning, LLMs would produce wrong intended responses, e.g.:
- 7. Fine-tuning for LLMs (2 of 2) 7 Prompt: What is Yann LeCun an expert in? LLM possible results: ● Yann has many publications in artificial intelligence, computer vision, and mobile robotics. ● Yann has worked in artificial intelligence, machine learning, and mobile robotics. ● Yann has researched artificial intelligence, computer vision, and computational neuroscience. Reinforcement Learning with Human Feedback (RLHF): corrects nuanced errors to be more in line with expected responses. Examples are collected and annotators select their preferred model outputs. This data is used to train a reward model, where the focus is on helpfulness and safety. source
- 8. Ways for the Rest of Us to Customize Pre-trained LLMs Parameter-efficient fine-tuning (PEFT): e.g. adapter modules, prompt tuning, sparse update methods, provides better accuracy with lower compute costs Few-shot In-context Learning (ICL): feed a small number of training examples as part of the input (computationally intensive) 8
- 9. Custom Fine-tuning with LoRA Observe that customization often changes a small subset of the original LLM parameters. LoRA = Low-Rank Adaptation of Large Language Models Fine-tune on a lower rank subset of parameters before adding to the pretrained weights Example to the left: Results showing LLaMA-2 can learn ViGGO (Video Game vernacular) Source 9 ← From original LoRA research paper Parameter-efficient fine-tuning example
- 10. LLM can modify its behavior based on previous prompts Exhibiting chain-of-thought, or reasoning, in prompts produces better responses. 10 Few-shot In-context Learning example
- 11. Custom options for LLMs (3 of 3) 11 Corpus of Human Knowledge (“The Internet”) Train a custom LLM Tune general purpose LLM Prompt general purpose LLM Modify prompts to incorporate or emphasize custom knowledge. These techniques are used in foundation models already too!
- 12. How do LLMs have a conversation? Research on LLM long term memory not implemented yet. Currently Chat LLM programs feed the previous queries and responses into each new prompt to provide a sense of conversation. Note: ● LLM input token limit: means earlier parts of the conversation are eventually forgotten ● Transformers do not “change their minds”: responses are auto-regressive (errors accumulate). ● Self-attention helps: More information in prompt leads to more specialized responses. 12 LLM input token limit ChatGPT allows ~4k words, GPT-4 allows ~32k words, Claude 2 allows ~75k words
- 13. Prompt expansion with Retrieval Augmented Generation (RAG) Rather than pass costly text with each prompt, RAG adds an information retrieval mechanism to augment the user prompt with relevant context info otherwise not available to the LLM, e.g.: ● real-time context (weather, location, etc.) ● user-specific information (website orders, status, etc.) ● relevant factual information (docs not in LLM training data - either private or updated after the LLM was trained). 13 This is accomplished by building a vector embedding index around input data using e.g., Langchain or Llama Index source
- 14. Summary Beyond an all-out retraining, there are two main techniques to improve LLM output relevance summarized as [ref]: ● Fine-tune model for form (e.g., LoRA) ● Prompt expansion for fact (e.g., RAG) Also, more LLMs are providing tools for custom data (see demo: NotebookLM) 14 Source
- 15. Additional References Personal LLAMA Getting started with LlamaIndex LlamaIndex Origin 15
- 16. Appendix 16
- 17. Example of Vector Embeddings with LlamaIndex from llama_index import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader('my-directory-of-docs').load_data() index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() results = query_engine.query("What is DeepHaiku? Be brief") print(results) 17 source