Tootfinder

xAI workers said they've encountered NSFW content, including AI-generated child sexual abuse material from Grok.
☑️ Musk's AI Tutors Describe 'Disgusting' Content Moderation Job - Business Insider
https://www.businessinsider.com/elon-musk-

Musk's AI tutors describe 'disgusting' content moderation job
Workers say they've faced sexually explicit content while xAI has marketed Grok to be deliberately provocative. Experts say the company should be cautious.

WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing
Yuhang Dai, Ziyu Zhang, Shuai Wang, Longhao Li, Zhao Guo, Tianlun Zuo, Shuiyuan Wang, Hongfei Xue, Chengyou Wang, Qing Wang, Xin Xu, Hui Bu, Jie Li, Jian Kang, Binbin Zhang, Lei Xie
https://arxiv.org/abs/2509.18004

WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing
The scarcity of large-scale, open-source data for dialects severely hinders progress in speech technology, a challenge particularly acute for the widely spoken Sichuanese dialects of Chinese. To address this critical gap, we introduce WenetSpeech-Chuan, a 10,000-hour, richly annotated corpus constructed using our novel Chuan-Pipeline, a complete data processing framework for dialectal speech. To facilitate rigorous evaluation and demonstrate the corpus's effectiveness, we also release high-qual…

Nice annotation of the US-Russia "peace plan" with regard to Ukraine.
https://samf.substack.com/p/the-witkoff-dmitriev-peace-plan-annotated?utm_campaign=post&showWelcomeOnShare=true

The Witkoff-Dmitriev peace plan annotated
We now know a bit more about the process which led to the new peace plan and we now have a copy of its contents, to which I will turn soon.

Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
Patrick Schmidt, Vasileios Belagiannis, Lazaros Nalpantidis
https://arxiv.org/abs/2509.17702

Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
Autonomous robotic systems applied to new domains require an abundance of expensive, pixel-level dense labels to train robust semantic segmentation models under full supervision. This study proposes a model-agnostic Depth Edge Alignment Loss to improve Weakly Supervised Semantic Segmentation models across different datasets. The methodology generates pixel-level semantic labels from image-level supervision, avoiding expensive annotation processes. While weak supervision is widely explored in tr…

A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories
Haojun Yu, Youcheng Li, Zihan Niu, Nan Zhang, Xuantong Gong, Huan Li, Zhiying Zou, Haifeng Qi, Zhenxiao Cao, Zijie Lan, Xingjian Yuan, Jiating He, Haokai Zhang, Shengtao Zhang, Zicheng Wang, Dong Wang, Ziwei Zhao, Congying Chen, Yong Wang, Wangyan Qin, Qingli Zhu
https…

A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories
Breast ultrasound (BUS) is an essential tool for diagnosing breast lesions, with millions of examinations per year. However, publicly available high-quality BUS benchmarks for AI development are limited in data scale and annotation richness. In this work, we present BUS-CoT, a BUS dataset for chain-of-thought (CoT) reasoning analysis, which contains 11,439 images of 10,019 lesions from 4,838 patients and covers all 99 histopathology types. To facilitate research on incentivizing CoT reasoning, …

Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora
Robert Litschko, Verena Blaschke, Diana Burkhardt, Barbara Plank, Diego Frassinelli
https://arxiv.org/abs/2509.17855

Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora
Dialects exhibit a substantial degree of variation due to the lack of a standard orthography. At the same time, the ability of Large Language Models (LLMs) to process dialects remains largely understudied. To address this gap, we use Bavarian as a case study and investigate the lexical dialect understanding capability of LLMs by examining how well they recognize and translate dialectal terms across different parts-of-speech. To this end, we introduce DiaLemma, a novel annotation framework for c…

{annotater}: Annotate package load calls, so we can have an idea of the overall purpose of the libraries we’re loading: #rstats

Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks
Jiaman He, Zikang Leng, Dana McKay, Damiano Spina, Johanne R. Trippas
https://arxiv.org/abs/2510.06658

Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks
Many evaluations of large language models (LLMs) in text annotation focus primarily on the correctness of the output, typically comparing model-generated labels to human-annotated ``ground truth'' using standard performance metrics. In contrast, our study moves beyond effectiveness alone. We aim to explore how labeling decisions -- by both humans and LLMs -- can be statistically evaluated across individuals. Rather than treating LLMs purely as annotation systems, we approach LLMs as an alternat…

📣 Wir möchten sehr herzlich zum Workshop "Digitale Annotation von Grafiken mit Antelope" einladen! In dem Kurs werden die Funktionalitäten von Antelope anhand von Daten aus GESAH vorgestellt und es wird erläutert, wie diese für die eigene Forschung genutzt werden können.
📆 Der Kurs findet am Mittwoch, den 12.11., von 10–12 Uhr statt.
📥Anmeldungen sind noch bis morgen, 5.11. möglich!

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
Cameron Morin, Matti Marttinen Larsson
https://arxiv.org/abs/2510.12306

A large-scale, unsupervised pipeline for automatic corpus annotation using LLMs: variation and change in the English consider construction
As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable, unsupervised pipeline for automating grammatical annotation in voluminous corpora using large language models (LLMs). Unlike previous supervised and iterative approaches, our method employs a four-phase workflow: prompt engineering, pre-hoc evaluation, automated batch processing, and post-hoc…

Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks
Vijja Wichitwechkarn, Emlyn Williams, Charles Fox, Ruchi Choudhary
https://arxiv.org/abs/2509.24972

Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks
Recent advances in one-shot imitation learning have enabled robots to acquire new manipulation skills from a single human demonstration. While existing methods achieve strong performance on single-step tasks, they remain limited in their ability to handle long-horizon, multi-step tasks without additional model training or manual annotation. We propose a method that can be applied to this setting provided a single demonstration without additional model training or manual annotation. We evaluated…

*nochmalskicher*
https://www.instagram.com/reel/DSKoD3wiAF6/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ==

ISTB University of Vienna on Instagram: "We are deeply honoured and delighted that the South Asian, Tibetan, and Buddhist Studies Library has been selected as one of the distinguished institutions to receive the eighty-volume commemorative edition of the Tipitaka, published in Thailand in 2016 to mark the seventieth anniversary of His Majesty King Bhumibol’s accession to the throne. The Thai monarchy has long upheld a well-established tradition of commissioning, presenting, and receiving editions of the Pali Canon. In 1893, King Rama V commissioned the first printed edition of the Tipitaka in Thailand, which was subsequently presented as a gift to institutions in more than twenty-five countries. The 40-volume “King Bhumibol Edition” allows monks and Buddhist laity worldwide to chant the Tipiṭaka in a consistent, rule-based manner. It is accompanied by the 40-volume “Queen Sirikit Edition” which reproduces King Rama V’s use of Syām-Pāli annotation with additional notes. The ISTB library provides an ideal home for this new edition of the Tipitaka. With a collection of more than 70,000 volumes in over ninety Asian languages, it serves as a vital centre for research and teaching in South Asian, Tibetan, and Buddhist Studies at the University of Vienna."
20 likes, 0 comments - istb_univienna on December 12, 2025: "We are deeply honoured and delighted that the South Asian, Tibetan, and Buddhist Studies Library has been selected as one of the distinguished institutions to receive the eighty-volume commemorative edition of the Tipitaka, published in Thailand in 2016 to mark the seventieth anniversary of His Majesty King Bhumibol’s accession to the throne. The Thai monarchy has long upheld a well-established tradition of commissioning, presentin…

Micro1, which helps AI labs find experts for data annotation, says it has crossed $100M in annualized revenue and fielded investment offers at a $2.5B valuation (Anna Tong/Forbes)
http://www.forbes.com/sites/annatong/2025

This 24 Year Old Built A Multibillion-Dollar AI Training Empire In Eight Months
Ali Ansari’s decision to turn micro1’s AI recruitment assistant into a data labeling business spiked the company’s valuation from $80 million to $2.5 billion.

Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation
Teodor Chiaburu, Vipin Singh, Frank Hau{\ss}er, Felix Bie{\ss}mann
https://arxiv.org/abs/2509.24873

Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation
Uncertainty quantification is essential in human-machine collaboration, as human agents tend to adjust their decisions based on the confidence of the machine counterpart. Reliably calibrated model uncertainties, hence, enable more effective collaboration, targeted expert intervention and more responsible usage of Machine Learning (ML) systems. Conformal prediction has become a well established model-agnostic framework for uncertainty calibration of ML models, offering statistically valid confid…

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Sara Rosenthal, Maeda Hanafi, Yannis Katsis, Lucian Popa, Marina Danilevsky
https://arxiv.org/abs/2510.11897

A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Grounding conversations in existing passages, known as Retrieval-Augmented Generation (RAG), is an important aspect of Chat-Based Assistants powered by Large Language Models (LLMs) to ensure they are faithful and don't provide misinformation. Several benchmarks have been created to measure the performance of LLMs on this task. We present a longitudinal study comparing the feedback loop of an internal and external human annotator group for the complex annotation task of creating multi-turn RAG c…

BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet)
Tomas Ruiz, Siyao Peng, Barbara Plank, Carsten Schwemmer
https://arxiv.org/abs/2510.12516

BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet)
Test-time scaling is a family of techniques to improve LLM outputs at inference time by performing extra computation. To the best of our knowledge, test-time scaling has been limited to domains with verifiably correct answers, like mathematics and coding. We transfer test-time scaling to the LeWiDi-2025 tasks to evaluate annotation disagreements. We experiment with three test-time scaling methods: two benchmark algorithms (Model Averaging and Majority Voting), and a Best-of-N sampling method. T…

LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
Boyuan Long, Yueqi Wang, Hiloni Mehta, Mick Zomnir, Omkar Pathak, Changping Meng, Ruolin Jia, Yajun Peng, Dapeng Hong, Xia Wu, Mingyan Gao, Onkar Dalal, Ningren Han
https://arxiv.org/abs/2510.06657

LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
This paper presents a case study on deploying Large Language Models (LLMs) as an advanced "annotation" mechanism to achieve nuanced content understanding (e.g., discerning content "vibe") at scale within a large-scale industrial short-form video recommendation system. Traditional machine learning classifiers for content understanding face protracted development cycles and a lack of deep, nuanced comprehension. The "LLM-as-annotators" approach addresses these by significantly shortening developm…

Explainable Fault Localization for Programming Assignments via LLM-Guided Annotation
Fang Liu, Tianze Wang, Li Zhang, Zheyu Yang, Jing Jiang, Zian Sun
https://arxiv.org/abs/2509.25676

Explainable Fault Localization for Programming Assignments via LLM-Guided Annotation
Providing timely and personalized guidance for students' programming assignments, offers significant practical value for helping students complete assignments and enhance their learning. In recent years, various automated Fault Localization (FL) techniques have demonstrated promising results in identifying errors in programs. However, existing FL techniques face challenges when applied to educational contexts. Most approaches operate at the method level without explanatory feedback, resulting i…

SNAP: Towards Segmenting Anything in Any Point Cloud
Aniket Gupta, Hanhui Wang, Charles Saunders, Aruni RoyChowdhury, Hanumant Singh, Huaizu Jiang
https://arxiv.org/abs/2510.11565

SNAP: Towards Segmenting Anything in Any Point Cloud
Interactive 3D point cloud segmentation enables efficient annotation of complex 3D scenes through user-guided prompts. However, current approaches are typically restricted in scope to a single domain (indoor or outdoor), and to a single form of user interaction (either spatial clicks or textual prompts). Moreover, training on multiple datasets often leads to negative transfer, resulting in domain-specific tools that lack generalizability. To address these limitations, we present \textbf{SNAP} (…

PhishSSL: Self-Supervised Contrastive Learning for Phishing Website Detection
Wenhao Li, Selvakumar Manickam, Yung-Wey Chong, Shankar Karuppayah, Priyadarsi Nanda, Binyong Li
https://arxiv.org/abs/2510.05900

PhishSSL: Self-Supervised Contrastive Learning for Phishing Website Detection
Phishing websites remain a persistent cybersecurity threat by mimicking legitimate sites to steal sensitive user information. Existing machine learning-based detection methods often rely on supervised learning with labeled data, which not only incurs substantial annotation costs but also limits adaptability to novel attack patterns. To address these challenges, we propose PhishSSL, a self-supervised contrastive learning framework that eliminates the need for labeled phishing data during trainin…

Transfer Learning in Regression with Influential Points
Bingbing Wang, Jiaqi Wang, Yu Tang
https://arxiv.org/abs/2509.20272 https://arxiv.org/pdf/2509.2027…

Transfer Learning in Regression with Influential Points
Regression prediction plays a crucial role in practical applications and strongly relies on data annotation. However, due to prohibitive annotation costs or domain-specific constraints, labeled data in the target domain is often scarce, making transfer learning a critical solution by leveraging knowledge from resource-rich source domains. In the practical target scenario, although transfer learning has been widely applied, influential points can significantly distort parameter estimation for th…

Decoding the dark proteome: Deep learning-enabled discovery of druggable enzymes in Wuchereria bancrofti
Shawnak Shivakumar, Jefferson Hernandez
https://arxiv.org/abs/2510.07337

Decoding the dark proteome: Deep learning-enabled discovery of druggable enzymes in Wuchereria bancrofti
Wuchereria bancrofti, the parasitic roundworm responsible for lymphatic filariasis, permanently disables over 36 million people and places 657 million at risk across 39 countries. A major bottleneck for drug discovery is the lack of functional annotation for more than 90 percent of the W. bancrofti dark proteome, leaving many potential targets unidentified. In this work, we present a novel computational pipeline that converts W. bancrofti's unannotated amino acid sequence data into precise four…

Generation and annotation of item usage scenarios in e-commerce using large language models
Madoka Hagiri, Kazushi Okamoto, Koki Karube, Kei Harada, Atsushi Shibata
https://arxiv.org/abs/2510.07885

Generation and annotation of item usage scenarios in e-commerce using large language models
Complementary recommendations suggest combinations of useful items that play important roles in e-commerce. However, complementary relationships are often subjective and vary among individuals, making them difficult to infer from historical data. Unlike conventional history-based methods that rely on statistical co-occurrence, we focus on the underlying usage context that motivates item combinations. We hypothesized that people select complementary items by imagining specific usage scenarios an…

LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
Yukun Yang
https://arxiv.org/abs/2509.26145

LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
Depression is a major global public health challenge and its early identification is crucial. Social media data provides a new perspective for depression detection, but existing methods face limitations such as insufficient accuracy, insufficient utilization of time series features, and high annotation costs. To this end, this study proposes the LMILAtt model, which innovatively integrates Long Short-Term Memory autoencoders and attention mechanisms: firstly, the temporal dynamic features of us…

Discovering Self-Regulated Learning Patterns in Chatbot-Powered Education Environment
Yilin Lyu, Ren Ding
https://arxiv.org/abs/2510.01275 https://arxiv.or…

Discovering Self-Regulated Learning Patterns in Chatbot-Powered Education Environment
The increasing adoption of generative AI (GenAI) tools such as chatbots in education presents new opportunities to support students' self-regulated learning (SRL), but also raises concerns about how learners actually engage in planning, executing, and reflection when learning with a chatbot. While SRL is typically conceptualized as a sequential process, little is known about how it unfolds during real-world student-chatbot interactions. To explore this, we proposed Gen-SRL, an annotation schema…

nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentation
Yi Yang
https://arxiv.org/abs/2509.19746

nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentation
Semi-supervised learning (SSL) has emerged as a promising paradigm in medical image segmentation, offering competitive performance while substantially reducing the need for extensive manual annotation. When combined with active learning (AL), these strategies further minimize annotation burden by selectively incorporating the most informative samples. However, conventional SSL_AL hybrid approaches often rely on iterative and loop-based retraining cycles after each annotation round, incurring si…

needLR: Long-read structural variant annotation with population-scale frequency estimation
Jonas A. Gustafson, Jiadong Lin, Evan E. Eichler, Danny E. Miller
https://arxiv.org/abs/2512.08175

An Annotation Scheme for Factuality and its Application to Parliamentary Proceedings
Gili Goldin, Shira Wigderson, Ella Rabinovich, Shuly Wintner
https://arxiv.org/abs/2509.26406

An Annotation Scheme for Factuality and its Application to Parliamentary Proceedings
Factuality assesses the extent to which a language utterance relates to real-world information; it determines whether utterances correspond to facts, possibilities, or imaginary situations, and as such, it is instrumental for fact checking. Factuality is a complex notion that relies on multiple linguistic signals, and has been studied in various disciplines. We present a complex, multi-faceted annotation scheme of factuality that combines concepts from a variety of previous works. We develope…

Curriculum Learning with Synthetic Data for Enhanced Pulmonary Nodule Detection in Chest Radiographs
Pranav Sambhu, Om Guin, Madhav Sambhu, Jinho Cha
https://arxiv.org/abs/2510.07681

Curriculum Learning with Synthetic Data for Enhanced Pulmonary Nodule Detection in Chest Radiographs
This study evaluates whether integrating curriculum learning with diffusion-based synthetic augmentation can enhance the detection of difficult pulmonary nodules in chest radiographs, particularly those with low size, brightness, and contrast, which often challenge conventional AI models due to data imbalance and limited annotation. A Faster R-CNN with a Feature Pyramid Network (FPN) backbone was trained on a hybrid dataset comprising expert-labeled NODE21 (1,213 patients; 52.4 percent male; me…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- ALISE: Annotation-Free LiDAR Instance Segmentation for Autonomous Driving
Yongxuan Lyu, Guangfeng Jiang, Hongsi Liu, Jun Liu

Clinical Uncertainty Impacts Machine Learning Evaluations
Simone Lionetti, Fabian Gr\"oger, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Ludovic Amruthalingam, Alexander A. Navarini, Marc Pouly
https://arxiv.org/abs/2509.22242

Clinical Uncertainty Impacts Machine Learning Evaluations
Clinical dataset labels are rarely certain as annotators disagree and confidence is not uniform across cases. Typical aggregation procedures, such as majority voting, obscure this variability. In simple experiments on medical imaging benchmarks, accounting for the confidence in binary labels significantly impacts model rankings. We therefore argue that machine-learning evaluations should explicitly account for annotation uncertainty using probabilistic metrics that directly operate on distribut…

Which Programming Language and Model Work Best With LLM-as-a-Judge For Code Retrieval?
Lucas Roberts, Denisa Roberts
https://arxiv.org/abs/2510.00324 https://

Which Programming Language and Model Work Best With LLM-as-a-Judge For Code Retrieval?
Code search is an important information retrieval application. Benefits of better code search include faster new developer on-boarding, reduced software maintenance, and ease of understanding for large repositories. Despite improvements in search algorithms and search benchmarks, the domain of code search has lagged behind. One reason is the high cost of human annotation for code queries and answers. While humans may annotate search results in general text QA systems, code annotations require s…

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Xin An, Ruijie Li, Qiao Ning, Hui Li, Qian Ma, Shikai Guo
https://arxiv.org/abs/2509.20240 https://

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Non-coding RNAs (ncRNAs) play pivotal roles in gene expression regulation and the pathogenesis of various diseases. Accurate classification of ncRNAs is essential for functional annotation and disease diagnosis. To address existing limitations in feature extraction depth and multimodal fusion, we propose HGMamba-ncRNA, a HyperGraphMamba-based multichannel adaptive model, which integrates sequence, secondary structure, and optionally available expression features of ncRNAs to enhance classificat…

Autoproof: Automated Segmentation Proofreading for Connectomics
Gary B Huang, William M Katz, Stuart Berg, Louis Scheffer
https://arxiv.org/abs/2509.26585 https://

Autoproof: Automated Segmentation Proofreading for Connectomics
Producing connectomes from electron microscopy (EM) images has historically required a great deal of human proofreading effort. This manual annotation cost is the current bottleneck in scaling EM connectomics, for example, in making larger connectome reconstructions feasible, or in enabling comparative connectomics where multiple related reconstructions are produced. In this work, we propose using the available ground-truth data generated by this manual annotation effort to learn a machine lear…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/9]:
- SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised...
Shuzheng Si, Zefan Cai, Shuang Zeng, Guoqiang Feng, Jiaxing Lin, Baobao Chang

LLM Trainer: Automated Robotic Data Generating via Demonstration Augmentation using LLMs
Abraham George, Amir Barati Farimani
https://arxiv.org/abs/2509.20070 https://

LLM Trainer: Automated Robotic Data Generating via Demonstration Augmentation using LLMs
We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of human demonstrations (as few as one) into a large robot dataset for imitation learning. Our approach decomposes demonstration generation into two steps: (1) offline demonstration annotation that extracts keyframes, salient objects, and pose-object relations; and (2) online keypose retargeting that adapts those keyframes to a new scene, given an ini…

Training Flow Matching Models with Reliable Labels via Self-Purification
Hyeongju Kim, Yechan Yu, June Young Yi, Juheon Lee
https://arxiv.org/abs/2509.19091 https://

Crossing Domains without Labels: Distant Supervision for Term Extraction
Elena Senger, Yuri Campbell, Rob van der Goot, Barbara Plank
https://arxiv.org/abs/2510.06838 https://…

Crossing Domains without Labels: Distant Supervision for Term Extraction
Automatic Term Extraction (ATE) is a critical component in downstream NLP tasks such as document tagging, ontology construction and patent analysis. Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. This highlights the need for more robust, scalable solutions and realistic evaluation settings. To address this, we introduce a comprehensive benchmark spanning seven diverse domains, enabling performance evalu…

Active Model Selection for Large Language Models
Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch, Torsten Hoefler, Nezihe Merve G\"urel
https://arxiv.org/abs/2510.09418 …

Active Model Selection for Large Language Models
We introduce LLM SELECTOR, the first framework for active model selection of Large Language Models (LLMs). Unlike prior evaluation and benchmarking approaches that rely on fully annotated datasets, LLM SELECTOR efficiently identifies the best LLM with limited annotations. In particular, for any given task, LLM SELECTOR adaptively selects a small set of queries to annotate that are most informative about the best model for the task. To further reduce annotation cost, we leverage a judge-based or…

Table Detection with Active Learning
Somraj Gautam, Nachiketa Purohit, Gaurav Harit
https://arxiv.org/abs/2509.20003 https://arxiv.org/pdf/2509.20003

Table Detection with Active Learning
Efficient data annotation remains a critical challenge in machine learning, particularly for object detection tasks requiring extensive labeled data. Active learning (AL) has emerged as a promising solution to minimize annotation costs by selecting the most informative samples. While traditional AL approaches primarily rely on uncertainty-based selection, recent advances suggest that incorporating diversity-based strategies can enhance sampling efficiency in object detection tasks. Our approach…

MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning
Tajamul Ashraf, Umair Nawaz, Abdelrahman M. Shaker, Rao Anwer, Philip Torr, Fahad Shahbaz Khan, Salman Khan
https://arxiv.org/abs/2510.08567

MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning
Vision language models (VLMs) are increasingly deployed as controllers with access to external tools for complex reasoning and decision-making, yet their effectiveness remains limited by the scarcity of high-quality multimodal trajectories and the cost of manual annotation. We address this challenge with a vision-centric agent tuning framework that automatically synthesizes multimodal trajectories, generates step-wise preference pairs, and trains a VLM controller for robust tool-use reasoning. …

PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch
Shangjian Yin, Shining Liang, Wenbiao Ding, Yuli Qian, Zhouxing Shi, Hongzhi Li, Yutao Xie
https://arxiv.org/abs/2510.06670

PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch
Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone for aligning large language models (LLMs). However, its effectiveness depends on high-quality instruction data. Most existing alignment datasets are either private or require costly human annotation, which limits reproducibility and scalability. Even with Reinforcement Learning from AI Feedback (RLAIF), concerns about data quality remain. Moreover, it is unclear how much data is actually required to fine-tune a base mode…

scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis
Ping Xu, Zaitian Wang, Zhirui Wang, Pengjiang Li, Ran Zhang, Gaoyang Li, Hanyu Xie, Jiajia Wang, Yuanchun Zhou, Pengfei Wang
https://arxiv.org/abs/2509.25884

scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis
Single-cell RNA sequencing (scRNA-seq) technology enables systematic delineation of cellular states and interactions, providing crucial insights into cellular heterogeneity. Building on this potential, numerous computational methods have been developed for tasks such as cell clustering, cell type annotation, and marker gene identification. To fully assess and compare these methods, standardized, analysis-ready datasets are essential. However, such datasets remain scarce, and variations in data …

Human-Annotated NER Dataset for the Kyrgyz Language
Timur Turatali, Anton Alekseev, Gulira Jumalieva, Gulnara Kabaeva, Sergey Nikolenko
https://arxiv.org/abs/2509.19109 https://…

Unsupervised Active Learning via Natural Feature Progressive Framework
Yuxi Liu, Catherine Lalman, Yimin Yang
https://arxiv.org/abs/2510.04939 https://arxi…

Unsupervised Active Learning via Natural Feature Progressive Framework
The effectiveness of modern deep learning models is predicated on the availability of large-scale, human-annotated datasets, a process that is notoriously expensive and time-consuming. While Active Learning (AL) offers a strategic solution by labeling only the most informative and representative data, its iterative nature still necessitates significant human involvement. Unsupervised Active Learning (UAL) presents an alternative by shifting the annotation burden to a single, post-selection step…

PAL-Net: A Point-Wise CNN with Patch-Attention for 3D Facial Landmark Localization
Ali Shadman Yazdi, Annalisa Cappella, Benedetta Baldini, Riccardo Solazzo, Gianluca Tartaglia, Chiarella Sforza, Giuseppe Baselli
https://arxiv.org/abs/2510.00910

PAL-Net: A Point-Wise CNN with Patch-Attention for 3D Facial Landmark Localization
Manual annotation of anatomical landmarks on 3D facial scans is a time-consuming and expertise-dependent task, yet it remains critical for clinical assessments, morphometric analysis, and craniofacial research. While several deep learning methods have been proposed for facial landmark localization, most focus on pseudo-landmarks or require complex input representations, limiting their clinical applicability. This study presents a fully automated deep learning pipeline (PAL-Net) for localizing 5…

Discrete Facial Encoding: : A Framework for Data-driven Facial Display Discovery
Minh Tran, Maksim Siniukov, Zhangyu Jin, Mohammad Soleymani
https://arxiv.org/abs/2510.01662 htt…

Discrete Facial Encoding: : A Framework for Data-driven Facial Display Discovery
Facial expression analysis is central to understanding human behavior, yet existing coding systems such as the Facial Action Coding System (FACS) are constrained by limited coverage and costly manual annotation. In this work, we introduce Discrete Facial Encoding (DFE), an unsupervised, data-driven alternative of compact and interpretable dictionary of facial expressions from 3D mesh sequences learned through a Residual Vector Quantized Variational Autoencoder (RVQ-VAE). Our approach first extr…

Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
Matteo Fuoli, Weihang Huang, Jeannette Littlemore, Sarah Turner, Ellen Wilding
https://arxiv.org/abs/2509.24866

Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
Metaphor is a pervasive feature of discourse and a powerful lens for examining cognition, emotion, and ideology. Large-scale analysis, however, has been constrained by the need for manual annotation due to the context-sensitive nature of metaphor. This study investigates the potential of large language models (LLMs) to automate metaphor identification in full texts. We compare three methods: (i) retrieval-augmented generation (RAG), where the model is provided with a codebook and instructed to …

The InviTE Corpus: Annotating Invectives in Tudor English Texts for Computational Modeling
Sophie Spliethoff, Sanne Hoeken, Silke Schwandt, Sina Zarrie{\ss}, \"Ozge Ala\c{c}am
https://arxiv.org/abs/2509.22345

The InviTE Corpus: Annotating Invectives in Tudor English Texts for Computational Modeling
In this paper, we aim at the application of Natural Language Processing (NLP) techniques to historical research endeavors, particularly addressing the study of religious invectives in the context of the Protestant Reformation in Tudor England. We outline a workflow spanning from raw data, through pre-processing and data selection, to an iterative annotation process. As a result, we introduce the InviTE corpus -- a corpus of almost 2000 Early Modern English (EModE) sentences, which are enriched …

Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs
Yehonatan Pesiakhovsky, Zorik Gekhman, Yosi Mass, Liat Ein-Dor, Roi Reichart
https://arxiv.org/abs/2509.22582

Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs
Context-grounded hallucinations are cases where model outputs contain information not verifiable against the source text. We study the applicability of LLMs for localizing such hallucinations, as a more practical alternative to existing complex evaluation pipelines. In the absence of established benchmarks for meta-evaluation of hallucinations localization, we construct one tailored to LLMs, involving a challenging human annotation of over 1,000 examples. We complement the benchmark with an LLM…

SwissGPC v1.0 -- The Swiss German Podcasts Corpus
Samuel Stucki, Mark Cieliebak, Jan Deriu
https://arxiv.org/abs/2509.19866 https://arxiv.org/pdf/2509.1986…

SwissGPC v1.0 -- The Swiss German Podcasts Corpus
We present SwissGPC v1.0, the first mid-to-large-scale corpus of spontaneous Swiss German speech, developed to support research in ASR, TTS, dialect identification, and related fields. The dataset consists of links to talk shows and podcasts hosted on Schweizer Radio und Fernsehen and YouTube, which contain approximately 5400 hours of raw audio. After segmentation and weak annotation, nearly 5000 hours of speech were retained, covering the seven major Swiss German dialect regions alongside Stan…