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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'id', 'source'})
This happened while the json dataset builder was generating data using
/tmp/hf-datasets-cache/medium/datasets/62377710116975-config-parquet-and-info-ziqinghuang-uncheatable-7641f8f9/hub/datasets--ziqinghuang--uncheatable/snapshots/18a066490333e0732f9f843d64451c0ade84b7be/arxiv_computer_science_20250901to20250914.jsonl.gz
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: string
text: string
source: string
to
{'text': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1456, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1055, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'id', 'source'})
This happened while the json dataset builder was generating data using
/tmp/hf-datasets-cache/medium/datasets/62377710116975-config-parquet-and-info-ziqinghuang-uncheatable-7641f8f9/hub/datasets--ziqinghuang--uncheatable/snapshots/18a066490333e0732f9f843d64451c0ade84b7be/arxiv_computer_science_20250901to20250914.jsonl.gz
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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Similarity-based Outlier Detection for Noisy Object Re-Identification Using Beta Mixtures
§ Abstract Object re-identification (Re-ID) methods are highly sensitive to label noise, which typically leads to significant performance degradation. We address this challenge by reframing Re-ID as a supervised image similarity t... |
Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Training
§ Abstract Segmentation models are important tools for the detection and analysis of lesions in brain MRI. Depending on the type of brain pathology that is imaged, MRI scanners can acquire ... |
§ Abstract Generative AI (GenAI) is reshaping work, but adoption remains largely individual and experimental rather than integrated into collaborative routines. Whether GenAI can move from individual use to collaborative work is a critical question for future organizations. Journalism offers a compelling site to examin... |
§ Abstract Large language models can influence users through conversation, creating new forms of dark patterns that differ from traditional UX dark patterns. We define LLM dark patterns as manipulative or deceptive behaviors enacted in dialogue. Drawing on prior work and AI incident reports, we outline a diverse set of... |
When FinTech Meets Privacy: Securing Financial LLMs with Differential Private Fine-Tuning
§ Abstract The integration of Large Language Models (LLMs) into financial technology (FinTech) has revolutionized the analysis and processing of complex financial data, driving advancements in real-time decision-making and analyti... |
In-Context Learning Enhanced Credibility Transformer
§ Abstract The starting point of our network architecture is the Credibility Transformer which extends the classical Transformer architecture by a credibility mechanism to improve model learning and predictive performance. This Credibility Transformer learns credibil... |
Cross-Layer Attention Probing for Fine-Grained Hallucination Detection
§ Abstract With the large-scale adoption of Large Language Models (LLMs) in various applications, there is a growing reliability concern due to their tendency to generate inaccurate text, i.e. hallucinations. In this work, we propose Cross-Layer Att... |
FINITE SCALAR QUANTIZATION ENABLES REDUNDANT AND TRANSMISSION-ROBUST NEURAL AUDIO COMPRESSION AT LOW BIT-RATES
§ Abstract Neural Audio Codecs (NACs) have become increasingly adopted in speech processing tasks due to their excellent rate-distortion performance and compatibility with Large Language Models (LLMs) as discr... |
LEARNING-BASED PLANNING FOR IMPROVING SCIENCE RETURN OF EARTH OBSERVATION SATELLITES
§ Abstract Earth observing satellites are powerful tools for collecting scientific information about our planet, however they have limitations: they cannot easily deviate from their orbital trajectories, their sensors have a limited fi... |
Difficulty-Aware Agent Orchestration in LLM-Powered Workflows
§ Abstract Large Language Model (LLM)-based agentic systems have shown strong capabilities across various tasks. However, existing multi-agent frameworks often rely on static or task-level workflows, which either over-process simple queries or underperform o... |
Compressing CNN models for resource-constrained systems by channel and layer pruning
§ Abstract Convolutional Neural Networks (CNNs) have achieved significant breakthroughs in various fields. However, these advancements have led to a substantial increase in the complexity and size of these networks. This poses a challe... |
Mechanism Design with Outliers and Predictions
§ Abstract We initiate the study of mechanism design with outliers, where the designer can discard z agents from the social cost objective. This setting is particularly relevant when some agents exhibit extreme or atypical preferences. As a natural case study, we consider ... |
Diffusion-Guided Multi-Arm Motion Planning
§ Abstract Multi-arm motion planning is fundamental for enabling arms to complete complex long-horizon tasks in shared spaces efficiently but current methods struggle with scalability due to exponential state-space growth and reliance on large training datasets for learned mod... |
Improving LLM Safety and Helpfulness using SFT and DPO: A Study on OPT-350M
§ Abstract This research investigates the effectiveness of alignment techniques, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and a combined SFT+DPO approach—on improving the safety and helpfulness of the OPT-350M languag... |
LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challenge
§ Abstract Biomedical question answering (QA) poses significant challenges due to the need for precise interpretation of specialized knowledge drawn from a vast, complex, and rapidly evolving corpus. In this work, we explo... |
Are Humans as Brittle as Large Language Models?
§ Abstract The output of large language models (LLM) is unstable, due to both non-determinism of the decoding process as well as to prompt brittleness. While the intrinsic non-determinism of LLM generation may mimic existing uncertainty in human annotations through distri... |
KV Cache Eviction from the Information Loss Perspective
§ Abstract KV Cache is commonly used to accelerate LLM inference with long contexts, yet its high memory demand drives the need for cache compression. Existing compression methods, however, are largely heuristic and lack dynamic budget allocation. To address this ... |
Large Language Model Hacking:
§ Abstract Large language models (LLMs) are rapidly transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis. However, LLM outputs vary significantly depending on the implementation choices made by researchers (e.g., mo... |
Augment to Segment: Tackling Pixel-Level Imbalance in Wheat Disease and Pest Segmentation
§ Abstract Accurate segmentation of foliar diseases and insect damage in wheat is crucial for effective crop management and disease control. However, the insect damage typically occupies only a tiny fraction of annotated pixels. T... |
PAnDA
§ Abstract Metric Differential Privacy (mDP) extends the local differential privacy (LDP) framework to metric spaces, enabling more nuanced privacy protection for data such as geo-locations. However, existing mDP optimization methods, particularly those based on linear programming (LP), face scalability challenge... |
Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing
§ Abstract Post-training language models (LMs) with reinforcement learning (RL) can enhance their complex reasoning capabilities without supervised fine-tuning, as demonstrated by DeepSeek-R1-Zero <cit.>. However, effectively utilizing ... |
RL Fine-Tuning Heals OOD Forgetting in SFT
§ Abstract The two-stage fine-tuning paradigm of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has empirically shown better reasoning performance than one-stage SFT for the post-training of Large Language Models (LLMs). However, the evolution and mechani... |
Semi-interval Comparison Constraints in Query Containment and Their Impact on Certain Answer Computation
§ Abstract We consider conjunctive queries with arithmetic comparisons (CQAC) and investigate the computational complexity of the problem: Given two CQAC queries, Q and Q', is Q' contained in Q? We know that, for CQ... |
ASL360: AI-Enabled Adaptive Streaming of Layered 360^∘ Video over UAV-assisted Wireless Networks
§ Abstract We propose ASL360, an adaptive deep reinforcement learning-based scheduler for on-demand video streaming to mobile VR users in next generation wireless networks. We aim to maximize the overall Quality of Experien... |
§ Abstract As Large Language Models (LLMs) are increasingly adopted as automated judges in benchmarking and reward modeling, ensuring their reliability, efficiency, and robustness has become critical. In this work, we present a systematic comparison of “thinking” and “non-thinking” LLMs in the LLM-as-a-judge paradigm u... |
TANGO: T
§ Abstract Visual navigation in robotics traditionally relies on globally-consistent 3D maps or learned controllers, which can be computationally expensive and difficult to generalize across diverse environments. In this work, we present a novel RGB-only, object-level topometric navigation pipeline that enable... |
The Linear Reliability Channel
§ Abstract We introduce and analyze a discrete soft-decision channel called the linear reliability channel (LRC) in which the soft information is the rank ordering of the received symbol reliabilities. We prove that the LRC is an appropriate approximation to a general class of discrete mo... |
LLM-Based Instance-Driven Heuristic Bias In the Context of a Biased Random Key Genetic Algorithm
§ Abstract Integrating Large Language Models (LLMs) within metaheuristics opens a novel path for solving complex combinatorial optimization problems. While most existing approaches leverage LLMs for code generation to creat... |
Representation Learning on Large Non-Bipartite Transaction Networks using GraphSAGE This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in Graph-Based Representations in Pattern Recognition. GbRPR 2025. Lecture Notes in ... |
§ Abstract Large Language Models (LLMs) have demonstrated substantial progress in biomedical and clinical applications, motivating rigorous evaluation of their ability to answer nuanced, evidence-based questions. We curate a multi-source benchmark drawing from Cochrane systematic reviews and clinical guidelines, includ... |
Miniature Microphone Array for Surface Wave Localization
Co-primary authors Carnegie Mellon University USA siqiz2@andrew.cmu.edu [1] Carnegie Mellon University USA Tsinghua University China xiyuxinz@andrew.cmu.edu [1] Carnegie Mellon University USA Michigan State University USA vuduc2@msu.edu [1] Carnegie Mellon Univer... |
§ Abstract Signature-based methods have recently gained significant traction in machine learning for sequential data. In particular, signature kernels have emerged as powerful discriminators and training losses for generative models on time-series, notably in quantitative finance. However, existing implementations do n... |
Analytical Design and Development of a Modular and Intuitive Framework for Robotizing and Enhancing the Existing Endoscopic Procedures
§ Abstract Despite the widespread adoption of endoscopic devices for several cancer screening procedures, manual control of these devices still remains challenging for clinicians, leadi... |
Beyond the Silence: How Men Navigate Infertility Through Digital Communities and Data Sharing
§ Abstract Men experiencing infertility face unique challenges navigating Traditional Masculinity Ideologies that discourage emotional expression and help-seeking. This study examines how Reddit's r/maleinfertility community h... |
Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts
§ Abstract Diffusion models have significantly advanced text-to-image generation, enabling the creation of highly realistic images conditioned on textual prompts and seeds. Given the considerable intellectual and economic value embedded in such prompts, p... |
Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning
§ Abstract Existing reinforcement learning (RL) methods face challenges in handling complex dynamical systems that require interactions at high frequencies or arbitrary time intervals. Continuous-time RL (CTRL) has emerged as a promising alternative... |
Data Skeleton Learning: Scalable Active Clustering with Sparse Graph Structures
§ Abstract In this work, we focus on the efficiency and scalability of pairwise constraint-based active clustering, crucial for processing large-scale data in applications such as data mining, knowledge annotation, and AI model pre-training... |
Arabic Large Language Models for Medical Text Generation
§ Abstract Efficient hospital management systems (HMS) are critical worldwide to address challenges such as overcrowding, limited resources, and poor availability of urgent health care. Existing methods often lack the ability to provide accurate, real-time medica... |
Efficient and Accurate Downfacing Visual Inertial Odometry
§ Abstract Visual Inertial Odometry (VIO) is a widely used computer vision method that determines an agent's movement through a camera and an IMU sensor. This paper presents an efficient and accurate VIO pipeline optimized for applications on micro- and nano-UA... |
REAL-WORLD MUSIC PLAGIARISM DETECTION WITH MUSIC SEGMENT TRANSCRIPTION SYSTEM
§ Abstract As a result of continuous advances in Music Information Retrieval (MIR) technology, generating and distributing music has become more diverse and accessible. In this context, interest in music intellectual property protection is in... |
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