Personality is a fundamental construct in psychology, reflecting an individual’s behavior, thinking, and emotional patterns. While previous researches have made progress in personality detection, their designed methods generally overlook the important connection between psychological knowledge “emotion regulation” and personality traits. Based on this, we propose a new personality detection method called EERPD. This method introduces the use of emotion regulation, a psychological concept highly correlated with personality, for personality prediction. By combining this concept with emotion features, EERPD retrieves few-shot examples and provides process CoTs for inferring labels from text. This approach enhances the understanding of LLM for personality implicit within text and improves the performance in personality detection. Experimental results demonstrate that EERPD significantly enhances the accuracy and robustness of personality detection, outperforming previous SOTA by 15.05/4.29 in average F1 on the two benchmark datasets.

It presents significant challenges to generate comprehensive and accurate Wikipedia articles for newly emerging events under real-world scenario. Existing attempts fall short either by focusing only on short snippets or by using metrics that are insufficient to evaluate real-world scenarios. In this paper, we construct WIKIGENBENCH, a new benchmark consisting of 1,320 entries, designed to align with real-world scenarios in both generation and evaluation. For generation, we explore a real-world scenario where structured, full-length Wikipedia articles with citations are generated for new events using input documents from web sources. For evaluation, we integrate systematic metrics and LLM-based metrics to assess the verifiability, organization, and other aspects aligned with real-world scenarios. Based on this benchmark, we conduct extensive experiments using various models within three commonly used frameworks: direct RAG, hierarchical structure-based RAG, and RAG with fine-tuned generation model. Experimental results show that hierarchical-based methods can generate more comprehensive content, while fine-tuned methods achieve better verifiability. However, even the best methods still show a significant gap compared to existing Wikipedia content, indicating that further research is necessary.

Large language model agents have exhibited exceptional performance across a range of complex interactive tasks.Recent approaches have utilized tuning with expert trajectories to enhance agent performance, yet they primarily concentrate on outcome rewards, which may lead to errors or suboptimal actions due to the absence of process supervision signals.In this paper, we introduce the Iterative step-level Process Refinement (IPR) framework, which provides detailed step-by-step guidance to enhance agent training.Specifically, we adopt the Monte Carlo method to estimate step-level rewards.During each iteration, the agent explores along the expert trajectory and generates new actions.These actions are then evaluated against the corresponding step of expert trajectory using step-level rewards.Such comparison helps identify discrepancies, yielding contrastive action pairs that serve as training data for the agent.Our experiments on three complex agent tasks demonstrate that our framework outperforms a variety of strong baselines.Moreover, our analytical findings highlight the effectiveness of IPR in augmenting action efficiency and its applicability to diverse models † .

Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations.Recent CRE studies have found that catastrophic forgetting arises from the model's lack of robustness against future analogous relations.To address the issue, we introduce rationale, i.e., the explanations of relation classification results generated by large language models (LLM), into CRE task.Specifically, we design the multi-task rationale tuning strategy to help the model learn current relations robustly.We also conduct contrastive rationale replay to further distinguish analogous relations.Experimental results on two standard benchmarks demonstrate that our method outperforms the state-of-the-art CRE models.Our code is available at https://github.com/WeiminXiong/ RationaleCL

Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction. However, most existing works focus on sentence-level coarse-grained JERE, which have limitations in real-world scenarios. In this paper, we construct a large-scale document-level fine-grained JERE dataset DocRED-FE, which improves DocRED with Fine-Grained Entity Type. Specifically, we redesign a hierarchical entity type schema including 11 coarse-grained types and 119 fine-grained types, and then re-annotate DocRED manually according to this schema. Through comprehensive experiments we find that: (1) DocRED-FE is challenging to existing JERE models; (2) Our fine-grained entity types promote relation classification. We make DocRED-FE with instruction and the code for our baselines publicly available at https://github.com/PKU-TANGENT/DOCRED-FE.