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natural language processing

9,568 papers

#artificial intelligence Preprint Open access Oct 2026

DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods

Much empirical legal research depends on translating unstructured text into structured variables. In corporate governance research as elsewhere, this translation has traditionally been performed by humans. Manual coding is time-consuming and costly. In corporate governance research, standardized commercial datasets off...

Jens Frankenreiter · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Probe, Don't Prompt: A Hidden-State Probe for Metadata Filtering in Multi-Meta-RAG

Multi-Meta-RAG improves retrieval for multi-hop question answering by filtering a vector store on metadata, specifically the news source and the publication date, which it extracts from each query using GPT-3.5. This extractor requires a proprietary API call for every query, and its output is not restricted to the allo...

Mykhailo Poliakov, Nadiya Shvai · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Same Evidence, Different Answers: Canonical-Context On-Policy Distillation for Multi-Turn Language Models

Large language models (LLMs) often solve a task when all instructions are given in a single prompt, but fail when the same information is revealed gradually across turns. When a clean FULL prompt and a RAW-SHARDED conversation contain the same complete user evidence, the model should still arrive at the same answer. We...

Zizhuo Lin, Quanling Liu, Jinsheng Quan et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

What a One-Hour Interview Does Not Tell an LLM About You: Interviews Elicit Traits, Not Decision Policies

Large language models are increasingly asked to stand in for particular people, and interviewing the person is the natural way to tell a model who they are. We test what a one-hour LLM-led adaptive interview tells a model about an individual's value-laden decisions. 46 adults completed such an interview and then answer...

Ruoxi Su, Yuhan Liu, Jingyu Hu · 0 citations

Dynamic Model Merging Made Slim

DiDi-Merging is proposed, which jointly allocates low-rank capacity to these components through differentiable rank selection, followed by data-free parameter-space refinement, followed by data-free parameter-space refinement.

Guo-Dong Du, Wan-Yu Lin · 3 citations
#artificial intelligence Preprint Open access Oct 2026

Abstract Compression of Conversational Audio Context for LLM-based ASR

Standard LLM-based speech recognition systems process utterances in isolation, limiting their ability to leverage conversational context. Here we study whether multimodal context from prior turns improves LLM-based ASR and how to represent that context efficiently. We find that, after supervised multi-turn training, co...

Shashi Kumar, Esa\'u Villatoro-Tello, Sergio Burdisso et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Detection Is Cheap, Routing Is Learned: Why Refusal-Based Alignment Evaluation Fails

Current alignment evaluation mostly measures whether models encode dangerous concepts and whether they refuse harmful requests. Both miss the layer where alignment often operates: routing from concept detection to behavioral policy. We study political censorship in Chinese-origin language models as a natural experiment...

Gregory N. Frank · 0 citations
#artificial intelligence Preprint Open access Oct 2026

CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning

Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforcement learning (RL)-based post-training on high-quality reasoning data. However, reproducing and extending these capabilities in open and scalable settings is hindered by three dat...

Xinyu Zhu, Yihao Feng, Yanchao Sun et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

TTCS: Test-Time Curriculum Synthesis for Self-Evolving

Test-Time Training offers a promising way to improve the reasoning ability of large language models (LLMs) by adapting the model using only the test questions. However, existing methods struggle with difficult reasoning problems for two reasons: raw test questions are often too difficult to yield high-quality pseudo-la...

Chengyi Yang, Zhishang Xiang, Yunbo Tang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Attention-Aligned Reasoning for Large Language Models

Large Language Models (LLMs) tend to generate a long reasoning chain when solving complex tasks. However, as the reasoning chain extends, critical intermediate steps and the original prompt will be buried in the context, receiving insufficient attention and leading to errors. In this work, we present ATAR, a novel reas...

Hongxiang Zhang, Yuan Tian, Tianyi Zhang · 0 citations
#artificial intelligence Review May 2025

A Survey of Generative Categories and Techniques in Multimodal Generative Models

This survey categorises six primary text-conditioned generative modalities and examines how four techniques, namely self-supervised learning, mixture of experts, reinforcement learning from human feedback, and chain-of-thought prompting, are adapted across them, distinguishing shared mechanisms from experimentally supp...

Longzhen Han, Awes Mubarak, Almas Baimagambetov et al. · 1 citation
#artificial intelligence Preprint Mar 2023

Typhoon: Gradient-Based Task-Specific Masking for Masked Language Models

Typhoon, a masking strategy that uses the gradient of the MLM loss with respect to one-hot token inputs to estimate online how much each token type contributes to the objective, is introduced and evaluated against random masking and whole-word masking.

Bruce Changlong Xu, Muhammed Shahir Abdurrahman, Hashem Elezabi · 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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