The present study profiled eight Serbian participants through a questionnaire, a deep interview, and a written self-presentation, recorded their reactions to sixty-eight social media posts, and asked four language models to predict those reactions under five prompt conditions varying profile content and instruction sty...
Ljubiša Bojić, Tijana Stanić, Joerg Matthes et al.· 0 citations
The contribution is not a more accurate judge, but a label-free way to tell when a judge has no basis for its answer, which is a label-free way to tell when a judge has no basis for its answer.
Large pretrained text-to-speech (TTS) models sound almost human for well-resourced languages, but much worse for languages that are rare in their training data. We study this quality gap for Khmer and Korean using VoxCPM2, a 2.4B parameter, tokenizer-free TTS model that joins a MiniCPM-4 language-model backbone with a...
Phannet Pov, Hyun Woo Park, Voneat Pen et al.· 0 citations
Overall, persona prompting affects interpretive framing more strongly than descriptive grounding, and profile-pair similarity patterns are strongly correlated for all three output types, although agreement is lowest for justifications.
Neemias B. da Silva, Matt Ratto, Myriam Delgado et al.· 0 citations
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Cascaded Automatic Speech Recognition - Large Language Model (ASR-LLM) pipelines remain popular for industrial Spoken Dialogue Systems (SDS), primarily because their decoupled design ensures perceptual verifiability. However, cascaded systems suffer from error propagation, as transcription failures inevitably cascade t...
Yizhou Peng, Ziyang Ma, Changsong Liu et al.· 0 citations
Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises: can frontier coding assistants reliably automate this evaluation process? Our study shows that simply prompting coding assistants is insuff...
Kang Zhou, Sangmin Woo, Haibo Ding et al.· 0 citations
In Natural Language Processing (NLP) classification tasks where a lack of labeled data is an issue, continued pretraining (CPT) of transformer models on unlabeled data is an established approach. In this paper, we have two aims.
(1) We describe our observations from continued pretraining of the Finnish BERT transform...
Rami Luisto, Liisa Pet\"ainen, Tommi Gr\"onholm et al.· 0 citations
We introduce DiscoPhon, a multilingual benchmark for evaluating unsupervised phoneme discovery from discrete speech units. DiscoPhon covers 6 dev and 6 test languages, chosen to span a wide range of phonemic contrasts. Given only 10 hours of speech in a previously unseen language, systems must produce discrete units th...
Maxime Poli, Manel Khentout, Angelo Ortiz Tandazo et al.· 0 citations
Temporarily ambiguous garden-path sentences ("While the team trained the striker wondered... ") are known to cause processing difficulty, which can manifest itself in a variety of reading behaviors (in-situ slowdowns, rereading), as well as in miscomprehension or outright rejection of the sentence as ungrammatical. Whi...
Dario Paape, Tal Linzen, Shravan Vasishth· 0 citations
Document-level Information Extraction (DocIE) aims to produce an output template with the entities, relations, and events of interest occurring in the given document. Standard practices include prompting decoder-only LLMs using greedy decoding to avoid output variability. Rather than treating this variability as a limi...
Mikel Zubillaga, Oscar Sainz, Oier Lopez de Lacalle et al.· 0 citations
Confidence estimation (CE) indicates how reliable the answers of large language models are and impacts user trust and decision-making. Existing evaluations mainly concern the alignment between confidence and correctness, but ignore the variability of language: confidence estimates should remain consistent under semanti...
Yuxi Xia, Dennis Ulmer, Terra Blevins et al.· 0 citations
Most automatic speech processing systems operate in ``open loop'' mode without user feedback about who said what, yet human-in-the-loop workflows can potentially enable higher accuracy. We propose an LLM-assisted in-meeting speaker correction system that lets users fix speaker attribution errors through brief correctiv...
Xinlu He, Yiwen Guan, Badrivishal Paurana 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.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
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.
MIT News · Artificial Intelligence· news.mit.eduSep 24, 2026
