This work benchmarks 41 open-weight LLMs across six behavioral economics games and shows that game-derived cooperative profiles robustly predict downstream performance in AI-for-Science tasks, indicating that cooperative disposition is a distinct, measurable property of LLMs not reducible to general ability.
Shivani Kumar, A. Bharathwaj, David Jurgens· arXiv.org· 1 citation
Automated analysis of customer feedback on social media is hindered by three challenges: the high cost of annotated training data, the scarcity of evaluation sets, especially in multilingual settings, and privacy concerns that prevent data sharing and reproducibility. We address these issues by developing a generalizab...
Pierre-Carl Langlais, Pavel Chizhov, Yannick Detrois et al.· 0 citations
Despite the success of reinforcement learning from human feedback (RLHF), existing reward modeling methods largely rely on explicit feedback, which is costly to collect and difficult to scale. This work studies implicit reward modeling, learning reward models from implicit user feedback, such as clicks, copies and skip...
Hao Wang, Haocheng Yang, Licheng Pan et al.· 0 citations
Large language models (LLMs) increasingly participate in morally sensitive decision-making, yet how they organize ethical frameworks across reasoning steps remains underexplored. We introduce moral reasoning trajectories, sequences of ethical framework invocations across intermediate reasoning steps, and analyze their...
Fan Huang, Haewoon Kwak, Jisun An· 0 citations
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Real-time video commentary generation provides textual descriptions of ongoing events in videos. It supports accessibility and engagement in domains such as sports, esports, and livestreaming. Commentary generation involves two essential decisions: what to say and when to say it. While recent prompting-based approaches...
Anum Afzal, Yuki Saito, Hiroya Takamura et al.· 0 citations
GapEval, a bidirectional benchmark designed to quantify the gap between understanding and generation capabilities, and quantitatively measure the cognitive coherence of the two “unified” directions, indicates that knowledge within UMMs often remains disjoint.
Chenlong Wang, Yu-Hang Chen, Zhihan Hu et al.· 2026 IEEE/CVF Conference on...· 11 citations· ⚡1
This work proposes Cross-Lingual Activation Steering (CLAS), a training-free inference-time intervention that selectively modulates neuron activations in existing models to unlock latent multilingual capacity in existing models without modification to model weights.
The introduction of BERT established encoder-only transformer models as a foundational paradigm in natural language processing. Encoder-only models remain the standard tool for classification, tagging and retrieval, where contextual representations and low inference cost matter more than text generation, yet Turkish la...
Melik\c{s}ah T\"urker, A. Ebrar K{\i}z{\i}lo\u{g}lu, Onur G\"ung\"or et al.· 0 citations
Language models (LMs) often struggle to generate diverse, human-like creative content, raising concerns about the long-term homogenization of human thought through repeated exposure to similar outputs. Yet scalable methods for evaluating LM output diversity remain limited, especially beyond narrow tasks such as random...
Liwei Jiang, Yuanjun Chai, Margaret Li et al.· 0 citations
The rapid proliferation of Large Language Models has intensified the challenge of distinguishing LLM-generated text from human writing in non-English languages. This study introduces VietBinoculars, a zero-shot detection framework coupling PhoGPT-4B observer and performer models with calibrated global decision threshol...
Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especially in healthcare, where clinically consequential decisions require not only accuracy but also interpretable, auditable rationales to meet safety, accountability, and regulatory req...
Chuan Li, Qianyi Zhao, Fengran Mo et al.· 0 citations
Understanding human emotions is central to user-facing AI applications, safety alignment, and the simulation of human behavior. As emotional stimuli shape high-stakes behavior in Large Language Models (LLMs), there is increasing interest in how models represent emotion concepts internally. Mechanistic accounts of these...
Sree Bhattacharyya, Evgenii Kuriabov, Lucas Craig et al.· 0 citations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
