The alignment of large language models (LLMs) is crucial not only for unlocking their potential in specific tasks but also for ensuring that responses meet human expectations and adhere to safety and ethical principles. To address the challenges of current alignment methodologies, we introduce self-evolution fine-tuning (SEFT) for LLM alignment, aiming to eliminate the need for annotated samples while retaining the stability and efficiency of SFT. SEFT first trains an adaptive reviser to elevate low-quality responses while maintaining high-quality ones. The reviser then gradually guides the policy’s optimization by fine-tuning it with enhanced responses. The method excels in utilizing unlimited unannotated data to optimize policies via supervised fine-tuning. Our experiments on AlpacaEval and MT-Bench demonstrate the effectiveness of SEFT and its advantages over existing alignment techniques.

Toxic span detection requires the detection of spans that make a text toxic instead of simply classifying the text. In this paper, a transformer-based model with auxiliary information is proposed for SemEval-2021 Task 5. The proposed model was implemented based on the BERT-CRF architecture. It consists of three parts: a transformer-based model that can obtain the token representation, an auxiliary information module that combines features from different layers, and an output layer used for the classification. Various BERT-based models, such as BERT, ALBERT, RoBERTa, and XLNET, were used to learn contextual representations. The predictions of these models were assembled to improve the sequence labeling tasks by using a voting strategy. Experimental results showed that the introduced auxiliary information can improve the performance of toxic spans detection. The proposed model ranked 5th of 91 in the competition. The code of this study is available at https://github.com/Chenrj233/semeval2021_task5