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3,366 papers
ECGQuest provides a reproducible benchmark for contextual ECG knowledge and shows that parameter-efficient fine-tuning can make smaller language models competitive with substantially larger commercial models.
Large language models are increasingly used to annotate datasets for training smaller, task-specialized models such as named entity recognition. While this method yields effective models, it assumes that the synthetic dataset is correctly annotated. In this work, we find that (i) current fine-tuning processes simply ig...
N-PPI is proposed, a pointwise extension of Prediction-Powered Inference that calibrates model predictions at inference time as a lightweight post-hoc layer, without re-training the underlying model, to make accurate check-worthiness detection substantially cheaper to operate at scale.
This work proposes Long Chain-of-Thought Graph Verifier (LCoT-GV), a graph-based framework that represents LCoTs as reasoning graphs, each node in the graph represents a reasoning step and the edges encode semantic and logical relations.
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Pera describes a persistent agent organized around perception and control components that continually perceive service-relevant signals from episodic task executions, internal context, and changes in the surrounding environment, and use these signals to construct lifecycle tasks.
This work presents S$^2$GE as an instance showing that diagnosis-driven interface design can improve native decoder usability and introduces an intervention triangle with three matched conditions: readable graph evidence, shuffled graph evidence, and no-graph input that separates evidence inclusion, structural readabil...
A fine-tuning-based Stratified Consistency Distillation approach that shows significant and consistent improvements in both Pass@K and the novel Equivalent Logical Similarity metrics, demonstrating the potential of advancing logical translation through consistency distillation.
Logical reasoning with large language models (LLMs) is a critical capability, as it reflects a system's ability to correctly deduce hypotheses from a given context using faithful deductive processes. However, LLM reasoning has often been shown to be sensitive to small surface-level variations in problem formulation, ra...
Manac-a-1B is the strongest model below the 7B scale, exceeding both Tucano-1b1 and Tucano-2b4 on LAMBADA-PT with large paired margins and is competitive on commonsense completion and near chance on multiple-choice reasoning, as are all small base models.
We present Arkios, a 1.04B-parameter dense transformer pretrained from scratch on 150B tokens of bilingual English-Nepali text, using a custom single-file C/CUDA training stack and a Devanagari-aware byte-level BPE tokenizer built for this project. On ARC-Easy and ARC-Challenge, Arkios exceeds three comparably sized op...
This work studies whether smaller language models can serve as efficient and reliable rubric-based judges, and compares three ways of extracting criterion-level judgments from small models: Generative verdicts, Yes/No Logprob margins, and Probe judges.
The DataFoundry is introduced, a framework for evolving data preparators through recursive self-improvement before large-scale data production, and it is found that recursively evolved preparators produce training data with higher downstream utility than baselines.
Generative AI
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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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.
A Blog post by IBM Research on Hugging Face
A Blog post by IBM Research on Hugging Face
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