Recently, large language models (LLMs) have shown great promise in sequential recommendation. Existing methods typically transform users' historical interactions into textual sequences and then feed these sequences into LLMs to generate recommended items. However, since the output of LLMs is limited to recommendations, the models must analyze user interactions and infer user preferences implicitly. This implicit approach constrains the expressive reasoning abilities of LLMs. Moreover, as the output of LLMs consists solely of recommended items, the resulting recommendations lack explainability. To address these issues, we propose RE2, which enables LLMs to explicitly generate reasoning content before providing recommendations. Making the reasoning process explicit helps elicit the reasoning abilities of LLMs and simultaneously enhances the explainability of recommendation results. RE2 consists of three steps: (1) Reasoning Collection, which collects reasoning data by guiding LLMs to generate both reasoning content and recommended items for each interaction sequence. (2) Pattern Imitation, which leverages the collected data to train LLMs via supervised fine-tuning to imitate the pattern of first generating reasoning content and then providing recommendations. (3) Pattern Internalization, which further internalizes this reasoning-and-recommendation pattern and enhances both recommendation performance and the rationality of the reasoning content through reinforcement learning. RE2 can be implemented under both the self-distillation and teacher-distillation frameworks, without requiring external user metadata such as user reviews. Experimental results demonstrate the effectiveness of RE2 in improving recommendation performance and in generating high-quality reasoning content. Furthermore, we show that RE2 can mitigate popularity bias while maintaining recommendation accuracy to some extent. Our data and code are available at https://github.com/zhiyuanc2001/RE2.

Recent studies show that large language models (LLMs) exhibit self-preference bias when serving as judges, meaning they tend to favor their own responses over those generated by other models.Existing methods typically measure this bias by calculating the difference between the scores a judge model assigns to its own responses and those it assigns to responses from other models.However, this approach conflates self-preference bias with response quality, as higher-quality responses from the judge model may also lead to positive score differences, even in the absence of bias.To address this issue, we introduce gold judgments as proxies for the actual quality of responses and propose the DBG score, which measures self-preference bias as the difference between the scores assigned by the judge model to its own responses and the corresponding gold judgments.Since gold judgments reflect true response quality, the DBG score mitigates the confounding effect of response quality on bias measurement.Using the DBG score, we conduct comprehensive experiments to assess self-preference bias across LLMs of varying versions, sizes, and reasoning abilities.Additionally, we investigate two factors that influence and help alleviate self-preference bias: response text style and the post-training data of judge models.Finally, we explore potential underlying mechanisms of self-preference bias from an attention-based perspective.Our code and data are available at https://github.com/ zhiyuanc2001/self-preference.Model B Gold Model A Judge Model 31.8% 68.2% 33.9% 66.1% 44.4% 55.6% Model A: Llama-3.1-8BModel B: Qwen2.5