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Showing papers from Microsoft (United States) Show all papers

88%Must read
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arXivMicrosoftU Illinois Urbana-ChampaignReward models & LLM-as-a-judge

StudentSim: Training LLM-based Student Simulators

StudentSim trains LLM student simulators via pooled training and per-student specialization, outperforming GPT-5.4 on behavioral fidelity and guidance responsiveness across chess, writing, and math.

Ke Yang, Chenglong Wang, Michel Galley, Chandan Singh and 3 more

Published Sep 1, 2026 · 0 citations · ▲ 495 on Hugging Face · Code ★ 53

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
78%Highly rated
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ACL 2026MicrosoftEmoryGriffithSpatio-temporal forecasting

STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning

STReasoner uses spatial-aware reinforcement learning to empower LLMs for spatio-temporal reasoning in time series, with large accuracy gains over proprietary models at low cost.

Juntong Ni, Shiyu Wang, Qi He, Ming Jin and 1 more

Published 2026 · 1 citation

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 0/5
76%Highly rated
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TPAMI 2025Nanyang TechnologicalMicrosoftVision-language models

Otter: A Multi-Modal Model With In-Context Instruction Tuning

Otter is a multi-modal model instruction-tuned with visual and textual in-context examples via the MIMIC-IT dataset, improving convergence and generalization on complex video and multi-image tasks.

Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang and 5 more

Published May 20, 2025 · 75 citations · Code ★ 3,443

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10/21 AI panelreviewers recommend it

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AI panel: 10 of 21 reviewers recommend it
lenient 5/5
medium 4/11
strict 1/5
71%Highly rated
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EMNLP 2024U WashingtonThe University of Texas at AustiMicrosoftHallucination & factuality

Teaching LLMs to Abstain across Languages via Multilingual Feedback

Multilingual feedback teaches LLMs to abstain from answering in low-resource languages and improves cross-lingual abstention without degrading performance.

Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding and 5 more

Published 2024 · 4 citations

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 0/5