Theory of Mind (ToM), the ability to infer mental states in others, is pivotal for human social cognition. Existing evaluations of ToM in LLMs are largely limited to English, neglecting the linguistic diversity that shapes human cognition. This limitation raises a critical question: can LLMs exhibit Multilingual Theory of Mind, which is the capacity to reason about mental states across diverse linguistic contexts? To address this gap, we present XToM, a rigorously validated multilingual benchmark that evaluates ToM across five languages and incorporates diverse, contextually rich task scenarios. Using XToM, we systematically evaluate LLMs (e.g., DeepSeek R1), revealing a pronounced dissonance: while models excel in multilingual language understanding, their ToM performance varies across languages. Our findings expose limitations in LLMs' ability to replicate human-like mentalizing across linguistic contexts.

Tianshi Zheng, Jiazheng Wang, Zihao Wang, Jiaxin Bai, Hang Yin, Zheye Deng, Yangqiu Song, Jianxin Li. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundamentally redefining research processes and human-AI collaboration.This survey systematically charts this burgeoning field, placing a central focus on the changing roles and escalating capabilities of LLMs in science.Through the lens of the scientific method, we introduce a foundational three-level taxonomy-Tool, Analyst, and Scientist-to delineate their escalating autonomy and evolving responsibilities within the research lifecycle.We further identify pivotal challenges and future research trajectories such as robotic automation, self-improvement, and ethical governance.Overall, this survey provides a conceptual architecture and strategic foresight to navigate and shape the future of AI-driven scientific discovery, fostering both rapid innovation and responsible advancement.

Debate is the process of exchanging viewpoints or convincing others on a particular issue. Recent research has provided empirical evidence that the persuasiveness of an argument is determined not only by language usage but also by communicator characteristics. Researchers have paid much attention to aspects of languages, such as linguistic features and discourse structures, but combining argument persuasiveness and impact with the social personae of the audience has not been explored due to the difficulty and complexity. We have observed the impressive simulation and personification capability of ChatGPT, indicating a giant pre-trained language model may function as an individual to provide personae and exert unique influences based on diverse background knowledge. Therefore, we propose a persona knowledge-aligned framework for argument quality assessment tasks from the audience side. This is the first work that leverages the emergence of ChatGPT and injects such audience personae knowledge into smaller language models via prompt tuning. The performance of our pipeline demonstrates significant and consistent improvement compared to competitive architectures.

The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream tasks, such as text summarization and text mining.Previous approaches often generate tables that directly replicate information from the text, limiting their applicability in broader contexts, as text-to-table generation in real-life scenarios necessitates information extraction, reasoning, and integration.However, there is a lack of both datasets and methodologies towards this task.In this paper, we introduce LIVESUM, a new benchmark dataset created for generating summary tables of competitions based on real-time commentary texts.We evaluate the performances of state-of-the-art LLMs on this task in both fine-tuning and zero-shot settings, and additionally propose a novel pipeline called T3 (Text-Tuple-Table ) to improve their performances.Extensive experimental results demonstrate that LLMs still struggle with this task even after fine-tuning, while our approach can offer substantial performance gains without explicit training.Further analyses demonstrate that our method exhibits strong generalization abilities, surpassing previous approaches on several other text-to-table datasets.

Privacy issues arise prominently during the inappropriate transmission of information between entities.Existing research primarily studies privacy by exploring various privacy attacks, defenses, and evaluations within narrowly predefined patterns, while neglecting that privacy is not an isolated, context-free concept limited to traditionally sensitive data (e.g., social security numbers), but intertwined with intricate social contexts that complicate the identification and analysis of potential privacy violations.The advent of Large Language Models (LLMs) offers unprecedented opportunities for incorporating the nuanced scenarios outlined in privacy laws to tackle these complex privacy issues.However, the scarcity of open-source relevant case studies restricts the efficiency of LLMs in aligning with specific legal statutes.To address this challenge, we introduce a novel framework, GOLDCOIN 1 , designed to efficiently ground LLMs in privacy laws for judicial assessing privacy violations.Our framework leverages the theory of contextual integrity as a bridge, creating numerous synthetic scenarios grounded in relevant privacy statutes (e.g., HIPAA), to assist LLMs in comprehending the complex contexts for identifying privacy risks in the real world.Extensive experimental results demonstrate that GOLD-COIN markedly enhances LLMs' capabilities in recognizing privacy risks across real court cases, surpassing the baselines on different judicial tasks.

In real applications, it is common to train deep neural networks (DNNs) on modest clusters. With the continuous increase of model size and batch size, the training of DNNs becomes challenging under restricted memory budget. The tensor partition and tensor rematerialization are two major memory optimization techniques to enable larger model size and batch size within the limited-memory constrain. However, the related algorithms failed to fully extract the memory reduction opportunity, because they ignored the invariable characteristics of dynamic computational graphs and the variation among the same size tensors at different memory locations. In this work, we propose MegTaiChi, a dynamic tensor-based memory management optimization module for the DNN training, which first achieves an efficient coordination of tensor partition and tensor rematerialization. The key feature of MegTaiChi is that it makes memory management decisions based on dynamic tensor access pattern tracked at runtime. This design is motivated by the observation that the access pattern to tensors is regular during training iterations. Based on the identified patterns, MegTaiChi exploits the total memory optimization space and achieves the heuristic, adaptive and fine-grained memory management. The experimental results show, MegTaiChi can reduce the memory footprint by up to 11% for ResNet-50 and 10.5% for GL-base compared with DTR. For the training of 6 representative DNNs, MegTaiChi outperforms MegEngine and Sublinear by 5X and 2.4X of the maximum batch sizes. Compared with FlexFlow, Gshard and ZeRo-3, MegTaiChi achieves 1.2X, 1.8X and 1.5X performance speedups respectively on average. For the million-scale face recognition application, Meg-TaiChi achieves 1.8X speedup compared with the optimal empirical parallelism strategy on 256 GPUs.