A Framework for Fine-Tuning LLMs using Heterogeneous Feedback
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910557555130368 |
|---|---|
| author | Aponte, Ryan Rossi, Ryan A. Guo, Shunan Dernoncourt, Franck Yu, Tong Chen, Xiang Mitra, Subrata Lipka, Nedim |
| author_facet | Aponte, Ryan Rossi, Ryan A. Guo, Shunan Dernoncourt, Franck Yu, Tong Chen, Xiang Mitra, Subrata Lipka, Nedim |
| contents | Large language models (LLMs) have been applied to a wide range of tasks, including text summarization, web navigation, and chatbots. They have benefitted from supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) following an unsupervised pretraining. These datasets can be difficult to collect, limited in scope, and vary in sample quality. Additionally, datasets can vary extensively in supervision format, from numerical to binary as well as multi-dimensional with many different values. We present a framework for fine-tuning LLMs using heterogeneous feedback, which has two main components. First, we combine the heterogeneous feedback data into a single supervision format, compatible with methods like SFT and RLHF. Next, given this unified feedback dataset, we extract a high-quality and diverse subset to obtain performance increases potentially exceeding the full dataset. We conduct extensive experiments to understand the effectiveness of these techniques for incorporating heterogeneous feedback, and demonstrate improvements from using a high-quality and diverse subset of the data. We find that our framework is able to improve models in multiple areas simultaneously, such as in instruction following and bias reduction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Framework for Fine-Tuning LLMs using Heterogeneous Feedback Aponte, Ryan Rossi, Ryan A. Guo, Shunan Dernoncourt, Franck Yu, Tong Chen, Xiang Mitra, Subrata Lipka, Nedim Computation and Language Machine Learning I.2.7 Large language models (LLMs) have been applied to a wide range of tasks, including text summarization, web navigation, and chatbots. They have benefitted from supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) following an unsupervised pretraining. These datasets can be difficult to collect, limited in scope, and vary in sample quality. Additionally, datasets can vary extensively in supervision format, from numerical to binary as well as multi-dimensional with many different values. We present a framework for fine-tuning LLMs using heterogeneous feedback, which has two main components. First, we combine the heterogeneous feedback data into a single supervision format, compatible with methods like SFT and RLHF. Next, given this unified feedback dataset, we extract a high-quality and diverse subset to obtain performance increases potentially exceeding the full dataset. We conduct extensive experiments to understand the effectiveness of these techniques for incorporating heterogeneous feedback, and demonstrate improvements from using a high-quality and diverse subset of the data. We find that our framework is able to improve models in multiple areas simultaneously, such as in instruction following and bias reduction. |
| title | A Framework for Fine-Tuning LLMs using Heterogeneous Feedback |
| topic | Computation and Language Machine Learning I.2.7 |
| url | https://arxiv.org/abs/2408.02861 |