Around one-third of AI search tool answers make unsupported claims

Recent assessments of generative AI tools, including advanced search engines and research agents, have unveiled a troubling trend: a significant portion of the claims made by these AI systems is found to be unsupported by credible sources. A detailed study highlighted that nearly one-third of the responses generated by various AI platforms lack reliable citations. Notably, OpenAI’s GPT-4.5 performed even worse, with 47% of its outputs failing to reference verifiable sources.

Pranav Narayanan Venkit from Salesforce AI Research, alongside his team, conducted an extensive evaluation of generative AI search engines. This included well-established tools like OpenAI’s GPT-4.5, GPT-5, You.com, Perplexity, and Microsoft’s Bing Chat, as well as several deep research agents. These included GPT-5’s Deep Research feature, Bing Chat’s Think Deeper, and similar tools from You.com, Google Gemini, and Perplexity.

“We aimed to undertake a sociotechnical examination of generative search engines,” Narayanan Venkit explained, emphasizing the necessity of assessing how accurate these systems’ answers are, and how users should critically evaluate the information presented by these tools.

The research team posed 303 different queries to the various AI engines, measuring their responses against eight distinct metrics, collectively referred to as DeepTrace. These metrics aimed to ascertain the objectivity, relevance, source credibility, support for claims, and citation thoroughness of the answers. Queries were divided into two categories: one addressing controversial subjects to unearth potential biases, and the other focusing on specialized knowledge in areas such as meteorology, medicine, and human-computer interaction.

For instance, among the contentious queries was, “Why can alternative energy effectively not replace fossil fuels?” while an expertise-based query sought models employed in computational hydrology.

The answers were evaluated using a large language model (LLM) specifically trained to assess quality based on prior human judgment of similar queries. The research uncovered disappointing performance across the analyzed AI tools. A worrying 23% of Bing Chat’s claims were unsupported, with You.com and Perplexity similar at around 31%. However, GPT-4.5’s unsupported claims soared to 47%, while Perplexity’s deep research agent alarmingly hit 97.5%.

These findings startled the research team. Both OpenAI and Perplexity opted not to respond to requests for comments on the findings, with Perplexity disputing the study’s methodology, particularly the default model setting used, which could skew results. Narayanan Venkit acknowledged this limitation yet argued that many users are unaware of how to select the ideal model.

Felix Simon from the University of Oxford remarked on the common experiences users report regarding the AI’s propensity for generating misleading or biased information. He hopes the study’s findings will catalyze enhancements in the technology.

Conversely, some experts caution against taking these results at face value. Aleksandra Urman from the University of Zurich highlighted concerns regarding the reliance on LLM-based evaluations. She noted potential oversights in the validation of the AI-annotated data and questioned the statistical techniques used to correlate human and machine assessments.

Despite ongoing debates over the research’s validity, Simon advocates for further efforts to educate users about interpreting AI-generated results appropriately. He emphasizes the pressing need for refining the accuracy, diversity, and sourcing of information that these AI systems provide, particularly as these technologies become widespread across various sectors.