cofacts

HackMD

Cofacts 會議記錄 - HackMD

# Cofacts 會議記錄 - [搜尋](<https://cse.google.com/cse?cx=71f4f7ee215d54fe6>)[target=_blank] ## 2024 -

arcads.ai

Arcads - Create engaging video ads using AI

Generate high-quality marketing videos quickly with Arcads, an AI-powered app that transforms a basic product link or text into engaging short video ads.

中央社 CNA

AI及深偽影音威脅激增 專家示警台灣強化應對工具 | 政治 | 中央社 CNA

微軟研究院（Microsoft Research）兩名專家近日應英國「經濟學人」期刊邀請撰文分析人工智慧（AI）、深偽影音等新興科技對民主政治構成的挑戰，提到台灣的政府機構和研究組織缺乏及時應對工具。

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Google

4 ways to use Search to check facts, images and sources online

For International Fact\u002DChecking Day, we’re sharing four Search features to help you evaluate information and get key context online.

台灣事實查核中心

【花蓮外海強震】地震災情影像真假一次看 查證地圖看這裡

image

HackMD

Cofacts 會議記錄 - HackMD

# Cofacts 會議記錄 - [搜尋](<https://cse.google.com/cse?cx=71f4f7ee215d54fe6>)[target=_blank] ## 2024 -

T客邦

停止讓AI再胡說八道，DeepMind 開發了「事實核查器」以糾正Claude、Gemini、GPT、PaLM-2的幻覺

解決 AI 聊天機器人幻覺問題的新方法：Google DeepMind 和史丹佛大學的 SAFE 系統

arXiv.org

Long-form factuality in large language models

Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics. To benchmark a model's long-form factuality in open domains, we first use GPT-4 to generate LongFact, a prompt set comprising thousands of questions spanning 38 topics. We then propose that LLM agents can be used as automated evaluators for long-form factuality through a method which we call Search-Augmented Factuality Evaluator (SAFE). SAFE utilizes an LLM to break down a long-form response into a set of individual facts and to evaluate the accuracy of each fact using a multi-step reasoning process comprising sending search queries to Google Search and determining whether a fact is supported by the search results. Furthermore, we propose extending F1 score as an aggregated metric for long-form factuality. To do so, we balance the percentage of supported facts in a response (precision) with the percentage of provided facts relative to a hyperparameter representing a user's preferred response length (recall). Empirically, we demonstrate that LLM agents can outperform crowdsourced human annotators - on a set of ~16k individual facts, SAFE agrees with crowdsourced human annotators 72% of the time, and on a random subset of 100 disagreement cases, SAFE wins 76% of the time. At the same time, SAFE is more than 20 times cheaper than human annotators. We also benchmark thirteen language models on LongFact across four model families (Gemini, GPT, Claude, and PaLM-2), finding that larger language models generally achieve better long-form factuality. LongFact, SAFE, and all experimental code are available at <https://github.com/google-deepmind/long-form-factuality>.

Medium

RAGFlow: Customizable, Credible, Explainable RAG engine based on document structure recognition…

Following the official open-sourcing of the AI-native database Infinity at the end of 2023, our end-to-end RAG solution, RAGFlow, was also…