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8,685 papers
As large language models (LLMs) are increasingly considered for automated assessment and feedback, understanding when LLM marking is valid is essential. We evaluate LLM-as-a-judge marking across four settings spanning three assessment formats - structured questions, written essays, and scientific plots - comparing GPT-...
ChEmbed is introduced, the first purpose-built family of domain-adapted text embedding models engineered for chemical literature retrieval, and represents a practical, lightweight, and reproducible embedding solution that effectively improves chemical literature retrieval.
The reachability problem in symmetric vector addition systems with states (VASS), where transitions are invariant under a group of permutations of coordinates, is investigated and the gain in complexity when the group arises as a combination of the trivial and symmetric groups is estimated.
D-STT, a simple yet effective defense algorithm that identifies and explicitly decodes safety trigger tokens of the given safety-aligned LLM to activate the model's learned safety patterns, effectively preserves model usability by introducing minimum intervention in the decoding process.
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The proposed Uncertainty-Calibrated MOPD (UCMOPD), which addresses limitations of standard MOPD through two complementary mechanisms, improves the general-capability average over standard MOPD by $4.48\% and $7.86\% while maintaining vertical-domain performance.
Across four system papers, the linked promotional page mainly helped identify what was being sold and added little to compliance-risk classification, which exposed sensitivity to rare labels, advertiser overlap across splits and substantial variation in the ST3 labels.
Jointly fine-tuning an LLM on meeting-summarisation corpora of widely varying size raises a question that prior work leaves confounded: when a domain-balanced training mixture helps, is the gain due to the distribution of tokens across domains, or merely to the volume of data seen? We disentangle these factors by const...
A per-token logits mask is applied that deterministically eliminates single-query column-use violations on the supported SQL fragment in a single decoding pass and achieves 0% Leakage Rate and Coverage up to 88.7% on Spider-CU, while staying within +10% tokens of direct prompting.
This work proposes a case-specific dynamic rubric framework that adaptively constructs MQM evaluation spaces for individual translation instances while dynamically selecting suitable subtype spaces and evaluation granularity for different cases.
JetSpec is proposed, a head-based SD framework that combines one-forward drafting efficiency with branch-wise causal conditioning that enables JetSpec to convert larger draft budgets into longer accepted prefixes and higher end-to-end speedup.
The results suggest that for state-of-the-art LLMs, identifying a solution to the problem is insufficient, and reliably executing the resulting plan still poses a distinct challenge.
One of the most central aspects of language processing is the ability to trade off between stored representations and abstract knowledge: one must retrieve stored representations, but also generate novel ones by applying productive rules. While recent work has examined abstract knowledge in language models, holistic st...
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.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
A Blog post by Liquid AI on Hugging Face
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