Human-Instruction-Free LLM Self-Alignment with Limited Samples
Hongyi Guo, Yuanshun Yao, Wei Shen, Jiaheng Wei, Xiaoying Zhang,, Zhaoran Wang, Yang Liu
TL;DR
This paper introduces a novel self-alignment algorithm for large language models that requires minimal human supervision, leveraging self-generated samples for iterative fine-tuning to improve alignment across various domains.
Contribution
The proposed method enables LLMs to self-align with limited samples without human-crafted instructions or labeled rewards, improving scalability and domain adaptability.
Findings
Effective in safety, truthfulness, and instruction-following benchmarks.
Achieves near-zero human supervision in alignment tasks.
Demonstrates strong domain adaptability and scalability.
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…
Peer Reviews
Decision·Submitted to ICLR 2025
1. The ISARA method works with very limited initial samples (<100), which significantly reduces human labor compared to traditional alignment methods. 2. It successfully works with smaller models (as small as 350M parameters), uses retrieval-augmented in-context learning to maintain quality without human guidance. It also implements an iterative self-improvement mechanism that enhances alignment over multiple cycles. 3. Experiments demonstrate effectiveness across multiple domains (safety, tru
1. Dataset usage: I notice that this paper splits the BeaverTails, TruthfulQA, and AlpacaEval datasets for both training and test, which is not appropriate. In real world, we cannot assume getting in-distribution samples as seed examples, especially for instruction-following. Most works in LLM alignment would choose other datasets, such as ShareGPT, Alpaca for training. 2. Weak baselines: This paper only compare ISARA with some weak baselines. The SFT baseline only finetunes the model with aro
**Originality:** The paper introduces ISARA, a novel LLM alignment method that reduces reliance on human-crafted instructions and reward models. It employs an iterative training loop that enhances alignment over time and combines retrieval-augmented in-context learning with iterative fine-tuning. ISARA demonstrates that smaller models (as small as 350M parameters) can achieve effective alignment, challenging previous norms. **Quality:** ISARA's effectiveness is backed by comprehensive empirical
**Insufficient Comparison with State-of-the-Art Methods:** My biggest concern is that the paper critiques existing alignment methods in its introduction but does not include comparisons in its experiments. While it suggests potential advantages over techniques like Self-Instruct and other recent data generation approaches, it only benchmarks against basic SFT and pre-trained models. This omission leaves the paper's claims about superiority unverified and its relative performance unclear. **Simp
1. The self-alignment method proposed in this paper can achieve alignment with near-zero human supervision, making it highly practical for real-world applications. 2. Experiments conducted across three key alignment benchmarks demonstrate the effectiveness of the self-alignment method. 3. The paper is clearly organized and well-written.
1. The novelty of the proposed method is relatively lacking. 2. The conducted experiments seem to be not sufficiently comprehensive.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Machine Learning and Data Classification