AI fact-checking is more accurate and 20 times cheaper than human effort, study finds


Researchers at the University of California, Berkeley and Google DeepMind have developed a method that demonstrates AI language models with access to search engines provide more accurate answers than human annotators.

The researchers used Google DeepMind’s Search-Augmented Factuality Evaluator (SAFE) tool to assess the factual accuracy of the responses. SAFE uses an AI agent to break down text responses into individual facts, check their relevance, and verify the relevant facts through Google searches, allowing it to assess the accuracy of each factual claim.

The LLM first breaks down the long answer into individual, self-contained facts. For each fact, the LLM determines whether it is relevant to answering the question and performs a Google search to verify it. | Image: Wei et al.

For the study, GPT-4 generated the publicly available “LongFact” dataset, containing 2,280 questions on 38 topics, which contains 2,280 questions on 38 topics and serves as the basis for evaluating the factual accuracy of long answers provided by large language models (LLMs).

A potential weakness of the system is that LongFact and SAFE depend on the capabilities of the language models used. If these models have weaknesses in following instructions or reasoning, this will affect the quality of the questions and scores generated. In addition, fact-checking depends on the capabilities and accesses of Google search.



Language models with internet access hallucinate less than humans

The researchers compared SAFE’s ratings for 16,011 individual facts with the ratings of human annotators from an earlier dataset.

They found that SAFE provided the same rating as human annotators for 72 percent of the facts, suggesting comparable performance in most cases. Moreover, in 100 instances where SAFE and humans disagreed, SAFE was correct in its assessment 76 percent of the time, while human annotators were only correct in 19 percent of cases, demonstrating SAFE’s fourfold superiority in these situations.

SAFE and humans agree about three-quarters of the time. However, when they disagree, SAFE is much more likely to be correct than humans (76% vs. 19%). Humans and machines were wrong about five out of 100 facts. | Image: Wei et al.

According to the researchers, when the AI model fails, it is primarily due to incorrect reasoning – since only GPT-3.5 was used here, there is still plenty of room for improvement.

In addition to its already superior performance, SAFE was more than 20 times cheaper than human annotators ($0.19 per answer vs. $4 per answer). The researchers attribute AI’s advantage to its ability to systematically retrieve and analyze vast amounts of information from the Web, while humans often rely on memory or subjective judgments, leading to more hallucinations.

According to the researchers, the SAFE system is better than humans at drawing logical conclusions from facts thanks to Google searches. | Image: Wei et al.

The study examined 13 language models from four model families (Gemini, GPT, Claude, and PaLM-2), with larger language models generally achieving better fact fidelity for long responses. GPT-4-Turbo, Gemini-Ultra and PaLM-2-L-IT-RLHF performed best.


results and code are available on GitHub.

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