large language models

Impact of Intermediate-Task Language Count on Zero-Shot Cross-Lingual Transfer Performance

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: What is the impact of varying the number of languages in intermediate-task training on zero-shot cross-lingual transfer performance, measured by XGLUE accuracy and F1 scores? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

mT5 Efficiency in Low-Resource Languages via Intermediate-Task Training

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How does intermediate-task training on English data influence the efficiency of mT5 in low-resource languages (e.g., inference speed, memory usage) while maintaining zero-shot cross-lingual reasoning performance on XTREME-R, measured by throughput (tokens/sec) and model latency? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

Impact of Intermediate-Task Language Count on Zero-Shot Cross-Lingual Transfer Performance

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: What is the impact of varying the number of languages in intermediate-task training on zero-shot cross-lingual transfer performance, measured by XGLUE accuracy and F1 scores? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

mT5 Efficiency in Low-Resource Languages via Intermediate-Task Training

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How does intermediate-task training on English data influence the efficiency of mT5 in low-resource languages (e.g., inference speed, memory usage) while maintaining zero-shot cross-lingual reasoning performance on XTREME-R, measured by throughput (tokens/sec) and model latency? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

Moral Concern Is a Response Regime: A Preregistered Cross-Model Test of Persona Framing in Large Language Models

Four large language models from four providers judged the same 20 ambiguous scenarios under a neutral frame and six loaded personas, across 11,200 main trials. The prediction came from Motivated Violation Construction (MVC), a theory of human moral cognition on which a loaded actor identity can move ambiguous behaviour into the violation category. A registered test-retest gate fixed the confirmatory panel, and semantically inert stimuli tested for frame-driven responding with no behaviour to classify. The models occupied sharply different operating ranges. Two classified almost every scenario as concerning under the neutral frame, leaving little headroom for the predicted increase, and one failed the correlation-based stability gate under near-ceiling responding. Across the retained three-model panel, none of four MVC predictions was supported, and the registered adjudication favoured valence priming over construction. On inert stimuli, three models almost never expressed concern, while one did so on roughly a third of trials and more often under loaded personas. The transfer test was therefore not supported. Whether that is also a refutation turns on detectability, which differed sharply across the retained panel. The durable result is a measurement result. Baseline, headroom, retest behaviour, sensitivity to prompt position and wording, refusal and missingness, and false entry on inert stimuli all differed by model. We call that configuration a model’s response regime, and it meant the same forced question carried different information in each system. A shared prompt is not a shared instrument, and a forced moral verdict measures the regime before it measures anything moral.

ChatEvac: End-to-End automation assessment for building evacuation safety based on large language and diffusion models

Graph neural network enhanced large language model for commonsense question answering

Large language model-enhanced multi-agent framework for seamless design and manufacturing integration

Financial news sentiment meets market data: A large language model-based approach to stock price prediction

Identifying central bank multi-objective preferences using large language models: Evidence from China

Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding of Indoor Building Spaces

Society is steadily embracing the idea of coinhabitant service robots in the built environment for assistance with household tasks and chores. In particular, the growing number of older adults, individuals with disabilities, and people requiring daily assistance has further created an urgent need for intelligent coinhabitant robots capable of supporting mobility, independence, and safety within physical living spaces. The growing complexity of modern built environments and the increasing prevalence of coinhabitant robots have underscored the need for advanced computational methods to support analysis, monitoring, and management. To provide users with meaningful assistance and clear scene understanding in such settings, robots must be able to perceive, interpret, and reason about their surroundings in a way that aligns with human spatial understanding. Place segmentation, which delineates spatial regions such as rooms or functional areas, together with semantic recognition, which assigns meaningful labels to these regions, forms the basis for accurate scene understanding in building-scale environments. Existing deep learning approaches have largely relied on closed-vocabulary detection systems, which are limited to a predefined set of categories of rooms and therefore struggle to interpret diverse or naturalistic descriptions of spaces. Furthermore, most current methods neglect the uncertainty inherent in scene recognition—for example, when rooms have overlapping functions (e.g., kitchen–dining areas) or when people use linguistically similar descriptions to refer to distinct spaces—thereby reducing their effectiveness in ambiguous or complex environments. To address these challenges, we propose an open-vocabulary semantic segmentation and classification pipeline that leverages vision–language models (VLMs) and large language models. Our framework follows a “segment–detect–select” paradigm for open-vocabulary scene understanding, consisting of three modules: (1) segmentation of distinct rooms and regions in the scene; (2) VLM-based similarity scoring between descriptive queries and segmented regions; and (3) adaptive conformal prediction to select among candidate regions while explicitly accounting for uncertainty. Evaluation results on the large-scale Matterport3D dataset, which comprises diverse photorealistic indoor environments, show that the proposed framework outperforms state-of-the-art open-vocabulary methods in scene segmentation [by 30% in AP50 and 5% in mean intersection over union (mIOU)], classification (by 200% in F1 score and 263% in mean average precision), and selection (by 50% in room mIoU). These advances are significant for built environments, as improved scene understanding directly supports safer, more adaptive, and more intuitive coinhabitant assistive agents for humans in complex indoor spaces.

CodeAssay: A Multi-Metric Benchmark with Audited Ground Truth for LLM Code Generation

These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures, and that CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.