Understanding whether large language models (LLMs) and the human brain converge on similar computational principles remains a fundamental and important question in cognitive neuroscience and AI. Do the brain-like patterns observed in LLMs emerge simply from scaling, or do they reflect deeper alignment with the architecture of human language processing? This study focuses on the sentence-level neural mechanisms of language models, systematically investigating how layer-wise representations in LLMs align with the dynamic neural responses during human sentence comprehension. By comparing hierarchical embeddings from 14 publicly available LLMs with fMRI data collected from participants, who were exposed to a naturalistic narrative story, we constructed sentence-level neural prediction models to identify the model layers most significantly correlated with brain region activations. Results show that improvements in model performance drive the evolution of representational architectures toward brain-like hierarchies, particularly achieving stronger functional and anatomical correspondence at higher semantic abstraction levels. These findings advance our understanding of the computational parallels between LLMs and the human brain, highlighting the potential of LLMs as models for human language processing.

Do Large Language Models (LLMs) genuinely grasp the compositional semantics of events, or do they rely on surface-level probabilistic heuristics?We investigate the Imperfective Paradox, a logical phenomenon where the past progressive aspect entails event realization for activities (e.g., running → ran) but not for accomplishments (e.g., building ↛ built).We introduce IMPERFECTIVENLI, a diagnostic dataset designed to probe this distinction across diverse semantic classes.Evaluating state-ofthe-art open-weight models, we uncover a pervasive Teleological Bias: models systematically hallucinate completion for goal-oriented events, even overriding explicit textual cancellation.Prompting interventions partially reduce this bias but trigger a calibration crisis, causing models to incorrectly reject valid entailments for atelic verbs.Representational analyses further show that while internal embeddings often distinguish progressive from simple past forms, inference decisions are dominated by strong priors about goal attainment.Taken together, our findings indicate that these current openweight LLMs operate as predictive narrative engines rather than faithful logical reasoners, and that resolving aspectual inference requires moving beyond prompting toward structurally grounded alignment.1 * Work done while visiting The University of Tokyo. 1 The data and code are available at: https://github. com/boleima/ImperfectiveParadox. Class Examples (Premise → Hypothesis)Entail?Telic P: The carpenter was building a gazebo.✗ No H: The carpenter built a gazebo.

Aspect-based sentiment analysis (ABSA) garnered growing research interest in multilingual contexts in the past.However, the majority of the studies lack more robust feature alignment and finer aspect-level alignment.In this paper, we propose a novel framework, MSMO: Multi-Scale and Multi-Objective optimization for cross-lingual ABSA.During multi-scale alignment, we achieve cross-lingual sentencelevel and aspect-level alignment, aligning features of aspect terms in different contextual environments.Specifically, we introduce codeswitched bilingual sentences into the language discriminator and consistency training modules to enhance the model's robustness.During multi-objective optimization, we design two optimization objectives: supervised training and consistency training, aiming to enhance cross-lingual semantic alignment.To further improve model performance, we incorporate distilled knowledge of the target language into the model.Results show that MSMO significantly enhances cross-lingual ABSA by achieving state-of-the-art performance across multiple languages and models. 1

Table Question Answering (TQA) aims to answer natural language questions about tabular data, often accompanied by additional contexts such as text passages.The task spans diverse settings, varying in table representation, question/answer complexity, modality involved, and domain.While recent advances in large language models (LLMs) have led to substantial progress in TQA, the field still lacks a systematic organization and understanding of task formulations, core challenges, and methodological trends, particularly in light of emerging research directions such as reinforcement learning.This survey addresses this gap by providing a comprehensive and structured overview of TQA research with a focus on LLM-based methods.We provide a comprehensive categorization of existing benchmarks and task setups.We group current modeling strategies according to the challenges they target, and analyze their strengths and limitations.Furthermore, we highlight underexplored but timely topics that have not been systematically covered in prior research.By unifying disparate research threads and identifying open problems, our survey offers a consolidated foundation for the TQA community, enabling a deeper understanding of the state of the art and guiding future developments in this rapidly evolving area.

Understanding pragmatics—the use of language in context—is crucial for developing NLP systems capable of interpreting nuanced language use. Despite recent advances in language technologies, including large language models, evaluating their ability to handle pragmatic phenomena such as implicatures and references remains challenging. To advance pragmatic abilities in models, it is essential to understand current evaluation trends and identify existing limitations. In this survey, we provide a comprehensive review of resources designed for evaluating pragmatic capabilities in NLP, categorizing datasets by the pragmatic phenomena they address. We analyze task designs, data collection methods, evaluation approaches, and their relevance to real-world applications. By examining these resources in the context of modern language models, we highlight emerging trends, challenges, and gaps in existing benchmarks. Our survey aims to clarify the landscape of pragmatic evaluation and guide the development of more comprehensive and targeted benchmarks, ultimately contributing to more nuanced and context-aware NLP models.

Bolei Ma, Berk Yoztyurk, Anna-Carolina Haensch, Xinpeng Wang, Markus Herklotz, Frauke Kreuter, Barbara Plank, Matthias Aßenmacher. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Multi-epitope vaccines (EVs) represent a versatile and promising approach for combating a wide range of diseases, including viral and bacterial infections, parasitic diseases, and cancer. A critical aspect of EV design is ensuring efficient proteasomal cleavage of the synthetic polypeptide to recover therapeutic epitopes for presentation to$\mathbf{T}$cells. This process is influenced by the selection and arrangement of epitopes, as well as the design of linkers joining them. Modern EV design frameworks leverage proteasomal cleavage predictors to optimize epitope recovery and vaccine efficacy. However, the predictive power of these tools remains a limiting factor, particularly for challenging cleavage sites such as N -terminals. In this work, we systematically review and benchmark recent advances in proteasomal cleavage prediction, focusing on deep learning-based methods. We evaluate a range of architectures, including MLPs, CNNs, LSTMs, and Transformers, alongside simpler baselines such as logistic regression. Our results demonstrate that while complex models achieve marginally higher predictive performance, simpler models remain competitive and offer significant computational efficiency. We also explore the impact of dataset size, window size, and training techniques, finding diminishing returns from increasingly larger datasets and more complex models. Notably, our benchmarking highlights substantial improvements in predicting N-terminal cleavage sites, which are often overlooked but critical for EV design. Our findings provide practical guidance for the development of nextgeneration proteasomal cleavage predictors and underscore the importance of considering the probabilistic nature of cleavage in EV design. By consolidating the state of the art and introducing efficient baselines, this work aims to advance the field of multiepitope vaccine design and support the development of more effective and personalized immunotherapies.11Code for all experiments is available at this repository: https://github.com/ziegleringo/cleavage_benchmark.

ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Large Language Models (LLMs) demonstrate exceptional language understanding and generation capabilities by learning from context. Leveraging the strong in-context learning (ICL) abilities of LLMs, prompt-based fine-tuning has proven to be effective for enhancing the adaptability and alignment of LLMs, especially in low-data scenarios. However, the billions of parameters resulting from layer stacking in LLMs present significant computational challenges, limiting the practicality of fine-tuning. To tackle this problem, we explore the application of layer-wise model pruning in prompt-based fine-tuning of LLMs for few-shot learning scenarios. Our approach involves dropping certain model layers and fine-tuning the model with the remaining layers. Surprisingly, we observe that even with fewer layers, LLMs maintain similar or better performance levels, particularly in prompt-based fine-tuning for text classification tasks. Remarkably, in certain cases, models with a single layer outperform their fully layered counterparts. These findings offer valuable insights for future work aimed at mitigating the size constraints of LLMs while preserving their performance, thereby opening avenues for significantly more efficient use of LLMs.

Bolei Ma, Ercong Nie, Shuzhou Yuan, Helmut Schmid, Michael Färber, Frauke Kreuter, Hinrich Schuetze. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.