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9,568 papers
The results show that culturally specific kinship generation remains difficult even when the relationship is explicitly stated and motivate generation-based evaluation alongside multiple-choice testing.
No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or combine their outputs. However, routing stops after selecting an initial model, while dense collaboration invokes peers on every query. We show that collaboration is non-...
PHONOS is a streaming module for real-time SA that performs accent neutralization in a privacy sense: reducing accent-origin cues by converting non-native segmental realizations toward a chosen target accent domain.
Probability-floor samplers substantially narrow the blind spot, demonstrating that the choice of truncation criterion matters for retaining human-used tokens and motivate decoding methods that preserve contextually appropriate low-probability choices while maintaining generation quality.
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VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark, is introduced to evaluate how vision-language models (VLMs) interpret and reason over visual and textual information beyond English, showing that the primary source of failure is not OCR, but instead multimodal grounding and reasoning over t...
This paper applies various NLP techniques to demonstrate feature de-biasing, feature compression, and industry classification in the commercial insurance context to show that NLP is not merely a supplementary tool but a foundational element of modern, data-driven insurance analytics.
It is proved that coherence optimization is equivalent to description-length regularization, and that among all such regularization schemes, coherence regularization with a prior derived from a pretrained model optimizes a lower bound of worst-case accuracy for semi-supervised learning.
A ModernBERT Bi-Encoder model is developed that achieves 98.4% accuracy on the PAN21 authorship verification task and identifies loss function, batch size, training duration, pre-trained model, input context length, and random text span data augmentation as important factors of model performance.
This work proposes the Agent-Editing World Model (AEWM), which models how reasoning and actions shape future task progress rather than simulating tool responses, and combines AEWM with EditAct, which integrates these capabilities with real execution.
Elastic Weight Consolidation preserves general capabilities best in both stages; on the 8B Spanish model the average score on general benchmarks drops 1.7 points versus 11.0 for standard fine-tuning, yet its scores for formality and grammatical gender control remain close to standard fine-tuning.
Weight-space post-training quantization is formulated as pre-deployment configuration selection using priced layer-output error and a trace reduction then yields a calibration-time price table and a budgeted price-guided selector, making fixed-geometry bit allocation a special case rather than the organizing problem.
The key-value (KV) cache of autoregressive transformers can be viewed as a fourth-order tensor spanning attention heads, tokens, features, and grouped layers. We measure the singular-value spectra of all four mode unfoldings on Mistral-7B-v0.3 and LLaMA-2-13B and compare four standard tensor decompositions: Tucker, CP,...
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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.
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