https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset https://www.datacamp.com/tutorial/fine-tuning-openais-gpt-4-step-by-step-guide finetuning GPT 4o jsonl format의 데이터를 만들어야 함. {"prompt": "<prompt text>", "completion": "<ideal generated text>"} {"messages": [{"role": "system", "content": "You are teaching assistant for Machine Learning. You should help to user to answer on his question."}, {"role": "user", "content": "What is machine learning?"}, {"role": "assistant", "content": "'Tis but the art of teaching machines to think, to learn from data most fine, and decisions to link."}]} {"messages": [{"role": "system", "content": "You are teaching assistant for Machine Learning. You should help to user to answer on his question."}, {"role": "user", "content": "How doth neural networks work?"}, {"role": "assistant", "content": "They're like the brains of yon digital beast, with layers many, and nodes that cease."}]} Upload training file Create a fine-tuned model Analyze fine-tuned model UI로도 관련 정보를 볼 수 있음. fine tuning 작업이 끝나면, job details의 'fine_tuned_model' 필드에서 모델 이름을 볼 수 있음. → 아래아 같이 모델 이름을 적고 활용하면 됨.

ENHANCING KNOWLEDGE RETRIEVAL WITH IN-CONTEXT LEARNING AND SEMANTIC SEARCH THROUGH GENERATIVE AI Method 1 : Generative text retrieval (GTR) 각 chunk에 word2vec 등으로 Embedding → vector database 구축 query embedding과 유사도 계산 → 가장 가까운 걸로. Generative tabular text retrieval (GTR-T) 먼저 database table과 meta data를 가져와서 .csv로 저장. query를 embedding하여 관련있는 table을 찾음 이 table을 Llm한테 줘서 적절한 sql 쿼리문을 생성함. Olio: A Semantic Search Interface for Data Repositories Intro Q&A, exploratory search, design search. 태블로를 활용한 시각화 → 썸네일 제공? Related works Semantic web search system keyword(structured query languages) based or NL based keyword based QUERIX - stanford CoreNLP parser + wordNet olio는 trends, location, groupings, aggregations, filters 등으로 intentfmf qnsfbgka. 의도를 특정 그룹으로 나누는 것 같음. 내가 하려는 것과 잘 어울리는지는 모르겠어서 일단 읽기 중단. Know where to go : make llm a relevant, responsible, and trustworthy searcher. Intro