Large Language Models (LLMs) have demonstrated impressive performance across diverse natural language processing (NLP) and reasoning tasks, yet the influence of psychological factors, such as personality traits, on their capabilities remains underexplored. Drawing on the Big Five personality framework, we investigate how personality configurations affect LLM performance across five task categories: interdisciplinary expert knowledge, safety and harmfulness detection, code generation, mathematical reasoning, and scientific problem solving. We control personality traits using two approaches: prompt-based induction with expert-crafted prompts and low-rank adaptation (LoRA) fine-tuning on a newly constructed dataset of 20,000 personality-conditioned instructions. Experiments on seven LLMs reveal systematic, task-dependent effects of personality. High Neuroticism consistently degrades robustness and generalization, while high Conscientiousness improves stability, particularly in safety-critical contexts. Larger models show stronger resilience under personality perturbations, and prompt-based control achieves a better balance between trait alignment and task performance than LoRA fine-tuning. These findings highlight the trade-offs between controllability, stability, and generalization in personality-aware LLMs.

The behavioral complexity of large language models (LLMs) has sparked growing interest in whether these models mirror human psychological traits. While prior work has explored the presence of personality in LLMs, most studies remain superficial, focus on linguistic style or isolated benchmarks, without probing deeper cognitive alignment with humans. In this paper, we present a comprehensive investigation into whether and how personality traits influence LLMs' behavior. Grounded in the Big Five personality traits, we introduce personalityconditioned prompts and evaluate their effects across both closed (e.g., reasoning, coding) and open-ended (e.g., writing) tasks. Our analysis spans task performance, linguistic style variation, alignment with human personality-ability correlations, and changes in internal reasoning structure. Experimental results reveal that: (i) personality traits affect LLM performance across all tasks, but only influence linguistic style in open-ended generation; (ii) personality-ability correlations in LLMs are broadly consistent with patterns observed in human psychology; and (iii) personality traits alter the behavior of LLMs, leading to different ways of reasoning across traits.

Large Language Models (LLMs) have demonstrated remarkable abilities in text comprehension and logical reasoning, indicating that the text representations learned by LLMs can facilitate their language processing capabilities. In neuroscience, brain cognitive processing signals are typically utilized to study human language processing. Therefore, it is natural to ask how well the text embeddings from LLMs align with the brain cognitive processing signals, and how training strategies affect the LLM-brain alignment? In this paper, we employ Representational Similarity Analysis (RSA) to measure the alignment between 23 mainstream LLMs and fMRI signals of the brain to evaluate how effectively LLMs simulate cognitive language processing. We empirically investigate the impact of various factors (e.g., pre-training data size, model scaling, alignment training, and prompts) on such LLM-brain alignment. Experimental results indicate that pre-training data size and model scaling are positively correlated with LLM-brain similarity, and alignment training can significantly improve LLM-brain similarity. Explicit prompts contribute to the consistency of LLMs with brain cognitive language processing, while nonsensical noisy prompts may attenuate such alignment. Additionally, the performance of a wide range of LLM evaluations (e.g., MMLU, Chatbot Arena) is highly correlated with the LLM-brain similarity.

Abstract An adverse drug event (ADE) is defined as an adverse reaction resulting from improper drug use, reported in various documents such as biomedical literature, drug reviews, and user posts on social media. The recent advances in natural language processing techniques have facilitated automated ADE detection from documents. However, the contextualized information and relations among text pieces are less explored. This paper investigates contextualized language models and heterogeneous graph representations. It builds a contextualized graph embedding model for adverse drug event detection. We employ different convolutional graph neural networks and pre-trained contextualized embeddings as the building blocks. Experimental results show that our methods can improve the performance by comparing recent ADE detection models, suggesting that a text graph can capture causal relationships and dependency between different entities in a document.

In this paper, we propose a gated iterative capsule network model for the ADR detection task, named GICN. To alleviate the impact caused by abbreviations and misspelled words, we add character embedding as part of the input. Most ADRs consist of multiple words, e.g., short-term memory dysfunction. Hence, we apply a convolutional neural network (CNN) to obtain the complete phrase information. To effectively extract deep semantic information, we introduce a capsule network with a gated iteration unit that clusters features from underlying to high capsules. The gated iteration mechanism can remember contextual information, which will be introduced when clustering features. Experimental results show that our approach can achieve significant performance improvement for ADR detection from social media text compared with other state-of-the-art works.

BACKGROUND: The adverse reactions that are caused by drugs are potentially life-threatening problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their detrimental impacts on patients. Detecting ADRs through clinical trials takes a large number of experiments and a long period of time. With the growing amount of unstructured textual data, such as biomedical literature and electronic records, detecting ADRs in the available unstructured data has important implications for ADR research. Most of the neural network-based methods typically focus on the simple semantic information of sentence sequences; however, the relationship of the two entities depends on more complex semantic information. METHODS: In this paper, we propose multihop self-attention mechanism (MSAM) model that aims to learn the multi-aspect semantic information for the ADR detection task. first, the contextual information of the sentence is captured by using the bidirectional long short-term memory (Bi-LSTM) model. Then, via applying the multiple steps of an attention mechanism, multiple semantic representations of a sentence are generated. Each attention step obtains a different attention distribution focusing on the different segments of the sentence. Meanwhile, our model locates and enhances various keywords from the multiple representations of a sentence. RESULTS: Our model was evaluated by using two ADR corpora. It is shown that the method has a stable generalization ability. Via extensive experiments, our model achieved F-measure of 0.853, 0.799 and 0.851 for ADR detection for TwiMed-PubMed, TwiMed-Twitter, and ADE, respectively. The experimental results showed that our model significantly outperforms other compared models for ADR detection. CONCLUSIONS: In this paper, we propose a modification of multihop self-attention mechanism (MSAM) model for an ADR detection task. The proposed method significantly improved the learning of the complex semantic information of sentences.