Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations

• URL: http://arxiv.org/abs/2404.10779v1
• Date: Sat, 23 Mar 2024 13:25:01 GMT
• Title: Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations
• Authors: Mathav Raj J, Kushala VM, Harikrishna Warrier, Yogesh Gupta,
• Abstract summary: We focus on fine tuning LLaMA, an open source LLM using proprietary documents and code from an enterprise repository. As part of this work, we aim to guide beginners on how to start with fine tuning an LLM for documentation and code. We also propose pre processing recipes for both documentation and code to prepare dataset in different formats.
• Score: 2.699900017799093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is a compelling necessity from enterprises for fine tuning LLMs (Large Language Models) o get them trained on proprietary domain knowledge. The challenge is to imbibe the LLMs with domain specific knowledge using the most optimial resource and cost and in the best possible time. Many enterprises rely on RAG (Retrieval Augmented Generation) which does not need LLMs to be ine-tuned but they are limited by the quality of vector databases and their retrieval capabilities rather than the intrinsic capabilities of the LLMs themselves. In our current work we focus on fine tuning LLaMA, an open source LLM using proprietary documents and code from an enterprise repository and use the fine tuned models to evaluate the quality of responses. As part of this work, we aim to guide beginners on how to start with fine tuning an LLM for documentation and code by making educated guesses on size of GPU required and options that are available for formatting the data. We also propose pre processing recipes for both documentation and code to prepare dataset in different formats. The proposed methods of data preparation for document datasets are forming paragraph chunks, forming question and answer pairs and forming keyword and paragraph chunk pairs. For code dataset we propose forming summary and function pairs. Further, we qualitatively evaluate the results of the models for domain specific queries. Finally, we also propose practical guidelines and recommendations for fine tuning LLMs.

Related papers

• Enhancing Discriminative Tasks by Guiding the Pre-trained Language Model with Large Language Model's Experience [4.814313782484443]
  Large Language Models (LLMs) and pre-trained Language Models (LMs) have achieved impressive success on many software engineering tasks. We use LLMs to generate domain-specific data, thereby improving the performance of pre-trained LMs on the target tasks.
  arXiv  Detail & Related papers  (2024-08-16T06:37:59Z)
• Open-domain Implicit Format Control for Large Language Model Generation [52.83173553689678]
  We introduce a novel framework for controlled generation in large language models (LLMs) This study investigates LLMs' capabilities to follow open-domain, one-shot constraints and replicate the format of the example answers. We also develop a dataset collection methodology for supervised fine-tuning that enhances the open-domain format control of LLMs without degrading output quality.
  arXiv  Detail & Related papers  (2024-08-08T11:51:45Z)
• CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
  We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
  arXiv  Detail & Related papers  (2024-04-08T21:15:36Z)
• Optimizing LLM Queries in Relational Workloads [58.254894049950366]
  We show how to optimize Large Language Models (LLMs) inference for analytical workloads that invoke LLMs within relational queries. We implement these optimizations in Apache Spark, with vLLM as the model serving backend. We achieve up to 4.4x improvement in end-to-end latency on a benchmark of diverse LLM-based queries on real datasets.
  arXiv  Detail & Related papers  (2024-03-09T07:01:44Z)
• FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability [70.84333325049123]
  FoFo is a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats. This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats.
  arXiv  Detail & Related papers  (2024-02-28T19:23:27Z)
• LAPDoc: Layout-Aware Prompting for Documents [3.523208537466128]
  We investigate the possibility to use purely text-based LLMs for document-specific tasks by using layout enrichment. Our results indicate that layout enrichment can improve the performance of purely text-based LLMs for document understanding by up to 15%.
  arXiv  Detail & Related papers  (2024-02-15T10:00:49Z)
• Making Large Language Models A Better Foundation For Dense Retrieval [19.38740248464456]
  Dense retrieval needs to learn discriminative text embeddings to represent the semantic relationship between query and document. It may benefit from the using of large language models (LLMs), given LLMs' strong capability on semantic understanding. We propose LLaRA (LLM adapted for dense RetrievAl), which works as a post-hoc adaptation of dense retrieval application.
  arXiv  Detail & Related papers  (2023-12-24T15:10:35Z)
• LLatrieval: LLM-Verified Retrieval for Verifiable Generation [67.93134176912477]
  Verifiable generation aims to let the large language model (LLM) generate text with supporting documents. We propose LLatrieval (Large Language Model Verified Retrieval), where the LLM updates the retrieval result until it verifies that the retrieved documents can sufficiently support answering the question. Experiments show that LLatrieval significantly outperforms extensive baselines and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2023-11-14T01:38:02Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.