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natural language processing

9,568 papers

#artificial intelligence Preprint Sep 2026

VAA-CSEC: Vote-guided Advantage Allocation for Chinese Semantic Error Correction

Chinese Semantic Error Correction (CSEC) targets semantic errors in Chinese text, which are typically more subtle and complex than spelling and grammatical errors but remain relatively underexplored. Existing LLM-based approaches face two recurring obstacles in this task: over-correction, and unclear interaction betwee...

Yi Han, Nan-Kai Lin, Juan Luo et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Group-Marginalized Self-Rewarding RL Drives Zero-Label Self-Evolving

GMAE aggregates reward realizations across possible contexts into a response-level distribution and estimates expected advantages, which exhibits stable learning, low extra cost, and good applicability across training datasets and RL backbones.

Yiming Wang, Yikang Liu, Qing-Yuan Tian et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Long-Term Memory-Guided Enhancement for Target Perception in Audio-Language Models

Inspired by long-term memory in human listening, Long-Term Memory-Guided Audio Enhancement (LTM-AE) is proposed to improve selective target perception by refining the audio representations of ALLMs without training to improve selective target perception.

Zhenhong Zhou, Xuan-Yue Zhao, You-Ji Liu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations

It is found that compression degrades reliability before solvability and proposed PAIR (Prompt Adaptation using Interventional Rollouts) for adapting structured compression prompts achieves the strongest cross-run reliability among compressed methods in every main benchmark-scope combination.

Guang-Hui Min, Liang Wu, Ming-Jia Shi et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Large-scale factor analysis shows machine intelligence is only partially interpretable

The findings go against current endeavors of defining, identifying, and targeting general intelligence as a tangible construct in language model development, since the strategy of targeting a single conceptual ability without support is left without support.

Faiz Ghifari Haznitrama, Afrizal Hasbi Azizy, Faeyza Rishad Ardi · 0 citations
#artificial intelligence Preprint Sep 2026

Similar Choices, Different Attention: Cross-Modal Associations in Humans and Vision-Language Models

Cross-modal associations are systematic pairings of features across modalities, such as the association of'bouba'with round shapes and'kiki'with sharp shapes. Prior work has compared humans and vision-language models (VLMs) on such associations, but often using different stimuli or tasks between humans and models. Here...

Su-Min Hong, Katsumi Ibaraki, Renee Shi et al. · 0 citations

Reliable Parallel Decoding in Masked Diffusion Language Models

Masked diffusion language models (MDLMs) can generate text efficiently by predicting multiple masked tokens in parallel, but predictions from the same forward pass are not necessarily reliable when committed together. We study when parallel commitment is reliable. Our diagnostics show that confidence alone does not det...

Zheng-Hao He, Bo-Han Liu, Guang-Zhi Xiong et al. · 0 citations
#artificial intelligence Review Sep 2026

Calibrated to Whom? Persona and Language Effects on Cultural Values in JEV

Decision-only language models return a probability for every answer option instead of generating text, which makes them attractive as survey respondents and as judges. We audit the cultural values of one such model, TypeSafe's JEV, with the Values Survey Module 2013. We asked it the 24 items as 12 matched Saudi and 12...

Bushra Asseri, Abdulaziz M. Asseri · 0 citations
#artificial intelligence Preprint Sep 2026

Training LLMs to Verbalize Evaluation Awareness

Verbalization training is introduced, a method for making LLMs less reticent about verbalizing evaluation awareness while avoiding to supervise the latent belief itself, and it is shown that VT-induced verbalizations reflect the richer knowledge acquired by the model.

Usman Anwar, Sahar Abdelnabi, David Krueger · 0 citations
#artificial intelligence Preprint Sep 2026

MoRE: Scaling mixture of experts with hardware-aware low-rank routing

This work proves that rank logarithmic in $M$ suffices for routing expressivity when the number of active experts is fixed, and is necessary up to precision factors, and introduces MoRE (Mixture of Rank-reduced-routed Experts), which factorizes the router weight matrix at rank r and reduces the routing cost to $O((h +...

Honam Wong, Surbhi Goel, Enric Boix-Adserà · 0 citations

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Microsoft Research Blog Oct 22, 2026

Flint-interactive: A language for creating interactive data visualizations

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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