1
教机器人干活，光“刷课时”可不够！灵初这次较真数据质量
专治人机动作对不齐
2
When Do LLMs Actually Help? Evaluating LLMs as Data Quality Annotators
一项针对LLM在数据质量标注中实际效用的系统性评估，揭示其能力边界与一致性问题。
arXiv:2608.18158v1 Announce Type: cross Abstract: LLMs have been increasingly used to catch data quality issues automatically, but we know very little…
3
Making LLM extraction trustworthy enough to act on
如何让大模型提取的数据可信到可行动？独立验证是关键一步，解决信任难题。
Article URL: https://www.zakihsn.com/writing/ai/can-i-trust-the-numbers Comments URL: https://news.ycombinator.com/item?id=49005971 Points: 1 # Commen…
4
Your AI Agents Are Only as Good as Your Database: Stop Upserting Messy JSON
你的AI代理性能取决于数据库质量——别再往里面塞杂乱的JSON了。
Why raw LLM payloads wreck your backend pipeline, and the exact Zod validation layer we use at SpaceAI360 to keep production databases clean. Let’s be…
5
Why this CEO thinks video games make better training data than the internet
游戏数据比互联网更适合训练AI？这家CEO提出颠覆性观点，值得关注
When it comes to achieving artificial general intelligence (AGI), large language models just don’t have what it takes. Models like ChatGPT a…
6
Reliable Mislabel Detection for Video Capsule Endoscopy Data
胶囊内窥镜数据标签常出错？这篇EMBC 2026论文提出可靠误标签检测方法，助你提升医疗AI数据质量，开源代码已出。
arXiv:2602.06938v2 Announce Type: replace-cross Abstract: The classification performance of deep neural networks relies strongly on access to large, a…
7
Online Data Selection for Instruction Tuning via Gaussian Processes
用高斯过程在线筛选指令微调数据，让大模型训练从拼数量转向拼质量，值得一读。
arXiv:2606.30077v1 Announce Type: new Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to da…
8
How much of an LLM-generated clinical corpus is actually new? A production-scale measurement of content redundancy for provenance classification
临床语料里有多少是AI抄来的？实测大模型生成内容的冗余度，给数据来源打假。
arXiv:2606.29605v1 Announce Type: new Abstract: Clinical machine learning increasingly relies on training corpora generated by large language models (…
9
Large Language Model-Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset
GPT-5.4辅助清理胸部CT标签，与人工标注一致性达96.4%，误标纠正率达74.2%。
arXiv:2606.22382v1 Announce Type: cross Abstract: Purpose: To evaluate whether large language model (LLM)-assisted label cleaning can identify label-r…
10
People training new AI models admit they just get chatbots to do it
一键识别AI生成文本，防止模型训练数据污染，确保数据集纯净可靠，守护AI未来质量
Article URL: https://www.newscientist.com/article/2531050-people-training-new-ai-models-admit-they-just-get-chatbots-to-do-it/ Comments URL: https://n…
11
Towards Anomaly Detection on Relational Data
针对关系数据的异常检测新方法，填补图结构数据异常识别空白。
arXiv:2606.18621v1 Announce Type: new Abstract: Relational databases are widely used for managing structured data in real-world systems. Detecting ano…
12
PostgreSQL 2200N Error: Causes and Solutions Complete Guide
在线XML内容验证工具，快速检测PostgreSQL 2200N错误，支持标签匹配与特殊字符转义检查，提升SQL XML数据质量。
PostgreSQL Error 2200N: invalid xml content PostgreSQL error 2200N: invalid xml content occurs when you attempt to insert or process malformed XML dat…
13
A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data
全面综述LLM生成数据的质量与可信度评估方法，涵盖框架、指标与挑战，是相关研究者的必读参考。
arXiv:2601.17717v3 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for generating data across various mod…
14
TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
新智能体框架TSQAgent通过专用推理提升时间序列数据质量评级，开创数据质量评估新范式。
arXiv:2606.03629v1 Announce Type: new Abstract: Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the mu…
15
ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
用主动学习策略精准筛选高价值偏好数据，大幅降低RLHF数据标注成本，大模型偏好对齐的新效率方案。
arXiv:2603.09692v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Langu…
16
dashi: A Python library for Dataset Shift Characterization to Support Trustworthy AI Development and Deployment
开源Python库「dashi」专攻数据集偏移表征，助力构建更可信的AI系统
arXiv:2605.31360v1 Announce Type: cross Abstract: The Artificial Intelligence (AI) life cycle requires a thorough understanding of the underlying data…
17
Token贵只因你喂给模型的垃圾太多了丨@亚马逊王晓野AIGC2026
Token贵不是因为模型，而是你喂的垃圾数据太多——亚马逊专家点破AIGC企业落地核心痛点
Demo从来都不难做，难的是让它在企业生产环境里真正跑起来
18
Token贵只因你喂给模型的垃圾太多了丨@亚马逊王晓野AIGC2026
揭秘Token成本居高不下的真相：不是模型贵，而是你喂的数据太“垃圾”，企业AI价值落地的关键在这里。
让世界模型迈向多智能体交互仿真
19
Data filtering methods for training language models
训练大模型的数据质量怎么保证？这篇论文系统梳理了主流数据过滤方法，想做高效训练的不要错过。
arXiv:2605.29807v1 Announce Type: cross Abstract: Data quality is a critical factor in the effectiveness of machine learning models. Label errors, pre…
20
GEM: Geometric Entropy Mixing for Optimal LLM Data Curation
用几何熵混合优化LLM数据筛选，新方法已提交ICML 2026
arXiv:2605.26121v1 Announce Type: cross Abstract: LLM pre-training efficacy increasingly depends on data composition rather than sheer volume. Yet, op…