Google and its DeepMind division, in collaboration with Stanford University, have introduced a novel approach to enhancing the factuality of responses generated by large language models (LLMs). This initiative, named the Search-Augmented Factuality Evaluator (SAFE), aims to use LLMs as automated evaluators for long-form factuality. SAFE combines Google Search and LLM queries to extract and verify individual claims in responses, demonstrating that LLM agents can achieve superhuman rating performance in fact-checking when given access to Google. The research also highlights that larger models tend to be more factual and that employing LLMs for fact-checking is significantly cheaper, being 20x cheaper than human annotators. The team has provided a comprehensive evaluation pipeline, including a new dataset and an autorater, showcasing that LLMs can effectively rate themselves better than humans. This development is particularly timely, given the increasing concern over the factual accuracy of information generated by AI systems.
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
Our new efforts is trying to address an elephant in the room for LLM: Given factuality/hallucination is so critical to the success of LLM, is there a quantitive evaluation to benchmark all existing LLMs in general? Hope our benchmark would be adopted and benchmarked as part of… https://t.co/NfkqTGRAoh
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