Google, in collaboration with DeepMind and Stanford, introduces a new method called Search-Augmented Factuality Evaluator (SAFE) to evaluate long-form factuality in large language models (LLMs). The method combines LLM agents with Google Search to verify claims, showing that LLMs can rate themselves better than humans and achieve superhuman rating performance. The research highlights that bigger models are more factual and LLMs are 20 times cheaper than humans for fact checking.
"Long-Form Factuality in Large Language Models" introduces a new approach to evaluating and benchmarking the factuality of long-form responses generated by large language models (LLMs). Key contributions: https://t.co/61SPVtboDN
Researchers from Google DeepMind and Stanford Introduce Search-Augmented Factuality Evaluator (SAFE): Enhancing Factuality Evaluation in Large Language Models Quick read: https://t.co/anXisulDKY Researchers from Google DeepMind and Stanford University have introduced a novel…
People and companies lie about AI. https://t.co/CTFindvjC4
DeepMind Unveils SAFE: An AI-Powered Tool for Fact-Checking LLMs #accuracy #AI #artificialintelligence #ChatGPT #Collaboration #DeepMind #Factcheckers #factchecking #GoogleSearch #llm #machinelearning #Media #methodology #opensource #Reliability #Safe https://t.co/RQ7hednLqH https://t.co/k6RPJa9UKr
Google is working on a new “Fact Checking” AI. The Search-Augmented Factuality Evaluator (SAFE). SAFE uses a large language model to break down generated text into individual facts, and uses Google Search to determine the accuracy of each claim. Yep. https://t.co/jrn8JCcw2B
New work on evaluating long form factuality 🎉. Our method SAFE combines google search and LLM queries to extract and verify individual claims in responses. Most excitingly, we show SAFE is cheaper💰 and more reliable ✅ than human annotators. https://t.co/ulSad7fs0b
New factuality research! We use LMs as annotators & search engines for grounding to create a realistic benchmark for evaluating long-form factuality. Simulating your daily queries to LMs about knowledge & truth. 🔍📊 #NLProc #FactChecking Check this out! 👇 https://t.co/UydBu8ObvC
We focus on long-form factuality in open domain, and so we show an entire evaluation pipeline with dataset + autorater + metric. The dataset was generated with LLMs and the autorater is an LLM agent with Google Search, demonstrating LLMs can rate themselves better than humans! https://t.co/DKwXxmBdFg
Our new work on evaluating and benchmarking long-form factuality. We provide a new dataset, an evaluation method, an aggregation metric that accounts for both precision and recall, and an analysis of thirteen popular LLMs (including Gemini, GPT, and Claude). We’re also… https://t.co/EHXmBY8LAE
New @GoogleDeepMind+@Stanford paper! 📜 How can we benchmark long-form factuality in language models? We show that LLMs can generate a large dataset and are better annotators than humans, and we use this to rank Gemini, GPT, Claude, and PaLM-2 models. https://t.co/A3vgEjbqTV https://t.co/x1tlgYlCdg
AIs have a bad reputation for truth, so three important findings in this paper: 1) "LLM agents can achieve superhuman rating performance" on fact checking when given access to Google! 2) Bigger models are more factual 3) LLMs are 20x cheaper than humans https://t.co/lSSMAjoOnF https://t.co/oAWuaZFNPA
[CL] Long-form factuality in large language models J Wei, C Yang, X Song, Y Lu, N Hu, D Tran, D Peng, R Liu, D Huang, C Du, Q V. Le [Google DeepMind] (2024) https://t.co/VtkDsWHUgs - The paper introduces LongFact, a new prompt set for benchmarking long-form factuality of… https://t.co/Ezd8dZWHW7
Great paper from Google-deepmind- "LONG - FORM FACTUALITY IN LARGE LANGUAGE MODELS" 📌 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). 📌 Demonstrate that LLM… https://t.co/TOhm3tCxrG
Google announces 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 https://t.co/SkcoK8qJaQ
Google presents Long-form factuality in large language models - Proposes that LLM agents can be used as automated evaluators for longform factuality - Shows that LLM agents can achieve superhuman rating performance repo: https://t.co/rlAIFSqfTU abs: https://t.co/L3CpeLaFpQ https://t.co/OSNpnr1BmP
“We assume that there are these smart people making AI, and therefore it's right. But the reality is, there are going to be some really big errors. And unless we have ethics and verification helping us get through that layer of error, it’s going to create so much chaos in the…