Curriculum learning is a widely adopted training strategy in natural language processing (NLP), where models are exposed to examples organized by increasing difficulty to enhance learning efficiency and performance. However, most existing approaches rely on manually defined difficulty metrics – such as text length – which may not accurately reflect the model’s own perspective. To overcome this limitation, we present a self-adaptive curriculum learning paradigm that prioritizes fine-tuning examples based on difficulty scores predicted by pre-trained language models (PLMs) themselves. Building on these scores, we explore various training strategies that differ in the ordering of examples for the fine-tuning: from easy-to-hard, hard-to-easy, to mixed sampling. We evaluate our method on four natural language understanding (NLU) datasets covering both binary and multi-class classification tasks.Experimental results show that our approach leads to faster convergence and improved performance compared to standard random sampling.

Large Language Models (LLMs) have shown remarkable capabilities in role-playing dialogues, yet they often struggle to maintain emotionally consistent and psychologically plausible character personalities. We present MECoT (Markov Emotional Chain-of-Thought), a framework that enhances LLMs’ ability to generate authentic personality-driven dialogues through stochastic emotional transitions. Inspired by dual-process theory, MECoT combines a Markov-chain-driven emotional processor for intuitive responses with an LLM-based reasoning mechanism for rational regulation, mapped onto a 12-dimensional Emotion Circumplex Model. The framework dynamically adjusts emotional transitions using personality-weighted matrices and historical context, ensuring both emotional coherence and character consistency. We introduce the Role-playing And Personality Dialogue (RAPD) dataset, featuring diverse character interactions with fine-grained emotional annotations, along with novel metrics for evaluating emotional authenticity and personality alignment. Experimental results demonstrate MECoT’s effectiveness, achieving 93.3% emotional accuracy on RAPD and substantially outperforming existing approaches. Our analysis reveals optimal emotional granularity (12-16 categories) and validates our data-driven personality optimization approach. Code and data are available at https://anonymous.4open.science/r/MECoT

In this paper, we describe our submission for the NLI4CT 2024 shared task on robust Natural Language Inference over clinical trial reports. Our system is an ensemble of nine diverse models which we aggregate via majority voting. The models use a large spectrum of different approaches ranging from a straightforward Convolutional Neural Network over fine-tuned Large Language Models to few-shot-prompted language models using chain-of-thought reasoning.Surprisingly, we find that some individual ensemble members are not only more accurate than the final ensemble model but also more robust.