Multimodal large language models (MLLMs) are increasingly capable in medical imaging, yet most focus on single-image settings. Clinical interpretation often requires integrating evidence across multiple images, such as different modalities, views, or time points. However, large-scale medical multi-image data and traini...
Zhen Chen, Yihang Fu, Rong Zhou et al.· 0 citations
Generative AI chatbots built for mental health could extend access to care, but evidence from real-world use is limited. We report a single-arm, naturalistic pilot of a foundation model trained for mental health, among 299 US adults with at least moderate depressive or anxiety symptoms who were followed for up to 12 mo...
Thomas D. Hull, Lizhe Zhang, Caitlin A. Stamatis et al.· 0 citations
LoRA fine-tuning yields higher sentence extraction accuracy and higher point estimates on all three reference-based correction similarity metrics in both languages, and several leading general-purpose reasoning models outperform all three evaluated medical-domain models on these two subtasks.
The facts a language model stores are tied to its parameter count, so small models that fit on edge devices fail on expert problems, which need specialized knowledge and follow multi-step procedures. Fine-tuning for a specific domain or task writes the knowledge into the parameters but must be repeated for every model...
Kenan Alkiek, David Jurgens, Vinod Vydiswaran· 0 citations
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Recent thinking models are capable of solving complex reasoning tasks by scaling test-time compute, but this scaling should be allocated in line with task difficulty. On one hand, short reasoning (underthinking) leads to errors on harder problems that require extended reasoning steps; but, excessively long reasoning (o...
Reliable model fingerprints are essential for protecting large language models (LLMs) against unauthorized redistribution and commercial misuse. In black-box deployment, verification is hindered by defensive filtering of suspected fingerprint queries, as well as by downstream model modifications that may weaken embedde...
Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMIXX adapts existing encoders through 18.8k source-linked triplets: paraphrases and Korean-English translations preserve meaning, while targeted financial rewrites introduce semant...
Hanwool Lee, Sara Yu, Yewon Hwang et al.· 0 citations
Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across adjacent layers, causing single-layer analyses to miss this cross-layer structure. Cross-layer sparse autoencoders (SAEs) address layer mixing but operate in continuous space, wh...
Ankur Garg, Xuemin Yu, Hassan Sajjad et al.· 0 citations
Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training data can be problematic, for example, when data are private or sensitive. In this work, we investigate...
Mansi Sakarvadia, Aswathy Ajith, Arham Khan et al.· 0 citations
This work bridges Emergent Communication and goal-conditioned RL, opening the door to wider applications of HER, and proves that the relabelling and predicate functions that ETHER derives from the RG avoid the degenerate solutions of the Hindsight RL problem, namely trivial predicates and collapsed relabelling function...
Kevin Denamganaï, Daniel Hernandez, Ozan Vardal et al.· arXiv.org· 0 citations
We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models. CombEval represents each problem as a typed Cofola specification over entities, combinatorial objects, object dependencies, and constraints, enabling controlled generation of natural-language counting problems with...
Yuxu Zhou, Ond\v{r}ej Ku\v{z}elka, Yuyi Wang et al.· 0 citations
The study of the Generalized Turing Test is initiated, a formal generalization of Turing's imitation game from humans to arbitrary interactive agents, yielding an inherently adaptive form of evaluation that does not rely on fixed benchmarks.
Daniel Mitropolsky, Riccardo Neumarker, Emanuele Rimoldi 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.
