Large language models (LLMs) are increasingly used in role-playing applications, yet evaluating whether they maintain character-consistent behavior over extended interactions remains challenging. Existing benchmarks mostly focus on short exchanges, static long-context inputs, or isolated aspects of role-playing, making long-horizon failures difficult to diagnose. We present REMP (Relationship-Emotion-Memory-Persona), a structured event-probing benchmark for long-horizon role-playing dialogue. REMP constructs multi-chapter dialogue trajectories from character profiles and embeds diagnostic probe events targeting persona consistency, emotional consistency, memory integrity, and relational continuity. We further introduce REMP-EVAL, an anchor-based multi-turn evaluation protocol that assesses model behavior after event triggers using dimension-specific references. The current release contains 160 characters organized into 80 dialogue trajectories, each averaging about 60 sessions and 800 turns. Experiments on 11 LLMs show that memory integrity and emotional consistency are the main bottlenecks, indicating that long-horizon role-playing requires not only long-context retrieval but also sustained affective and relational consistency.

Knowledge Tracing (KT) is a pivotal task in personalized education, aiming to predict students’ future performance based on their historical interactions. While prior work has focused on learning behavioral sequences using question IDs or surface-level textual features, these methods often fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge. To address this, we propose LLM-KT, a novel framework that integrates the reasoning power of Large Language Models (LLMs) with the sequential modeling strengths of traditional KT methods via multi-level plug-and-play alignment. Specifically, for task-level alignment, we design a plug-and-play instruction to leverage the rich knowledge and reasoning capacity of LLMs for the KT objective. For modality-level alignment, we introduce two mechanisms to integrate representations learned by traditional methods: (1) a Semantic History Projector that flexibly inserts compressed context embeddings into LLMs using question- and concept-specific tokens to capture long-term history; and (2) a Behavioral Dynamics Projector that enhances LLMs with sequential interaction patterns via a sequence adapter. Extensive experiments on four standard datasets demonstrate that LLM-KT achieves state-of-the-art performance, significantly outperforming over 20 competitive baselines.

Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text. However, a notable challenge in ABSA lies in precisely determining the aspects’ boundaries (start and end indices), especially for long ones, due to users’ colloquial expressions. We propose DiffusionABSA, a novel diffusion model tailored for ABSA, which extracts the aspects progressively step by step. Particularly, DiffusionABSA gradually adds noise to the aspect terms in the training process, subsequently learning a denoising process that progressively restores these terms in a reverse manner. To estimate the boundaries, we design a denoising neural network enhanced by a syntax-aware temporal attention mechanism to chronologically capture the interplay between aspects and surrounding text. Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. Our code is publicly available at https://github.com/Qlb6x/DiffusionABSA.

Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. In this paper, we focus on the structured sentiment analysis task that is released on SemEval-2022 Task 10. The task aims to extract the structured sentiment information (e.g., holder, target, expression and sentiment polarity) in a text. We propose a simple and unified model for both the monolingual and crosslingual structured sentiment analysis tasks. We translate this task into an event extraction task by regrading the expression as the trigger word and the other elements as the arguments of the event. Particularly, we first extract the expression by judging its start and end indices. Then, to consider the expression, we design a conditional layer normalization algorithm to extract the holder and target based on the extracted expression. Finally, we infer the sentiment polarity based on the extracted structured information. Pre-trained language models are utilized to obtain the text representation. We conduct the experiments on seven datasets in five languages. It attracted 233 submissions in monolingual subtask and crosslingual subtask from 32 teams. Finally, we obtain the top 5 place on crosslingual tasks.