Related papers: Human-Instruction-Free LLM Self-Alignment with Limited Samples

Human-Instruction-Free LLM Self-Alignment with Limited Samples

URL: http://arxiv.org/abs/2401.06785v1
Date: Sat, 6 Jan 2024 14:00:12 GMT
Title: Human-Instruction-Free LLM Self-Alignment with Limited Samples
Authors: Hongyi Guo, Yuanshun Yao, Wei Shen, Jiaheng Wei, Xiaoying Zhang, Zhaoran Wang, Yang Liu
Abstract summary: We propose an algorithm that can self-align large language models (LLMs) iteratively without active human involvement. Unlike existing works, our algorithm relies on neither human-crafted instructions nor labeled rewards, significantly reducing human involvement. We show that our method can unlock the LLMs' self-generalization ability to perform alignment with near-zero human supervision.
Score: 64.69906311787055
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Aligning large language models (LLMs) with human values is a vital task for LLM practitioners. Current alignment techniques have several limitations: (1) requiring a large amount of annotated data; (2) demanding heavy human involvement; (3) lacking a systematic mechanism to continuously improve. In this work, we study aligning LLMs to a new domain with limited samples (e.g. < 100). We propose an algorithm that can self-align LLMs iteratively without active human involvement. Unlike existing works, our algorithm relies on neither human-crafted instructions nor labeled rewards, significantly reducing human involvement. In addition, our algorithm can self-improve the alignment continuously. The key idea is to first retrieve high-quality samples related to the target domain and use them as In-context Learning examples to generate more samples. Then we use the self-generated samples to finetune the LLM iteratively. We show that our method can unlock the LLMs' self-generalization ability to perform alignment with near-zero human supervision. We test our algorithm on three benchmarks in safety, truthfulness, and instruction-following, and show good performance in alignment, domain adaptability, and scalability.

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