Why Generative Models Struggle with Negation
Generative models, like large language models, generate human-like text by predicting words based on patterns in datasets but struggle with negations, such as "not" in "I do not like apples." Training data favor affirmative statements, leaving fewer negated examples, so models poorly grasp how "not" inverts meaning, as seen in studies from Quanta Magazine and MIT Press Direct.
Though "not" is frequent, models treat it syntactically, not as a logical operator, causing errors like ignoring negation, with many tests showing inaccurate or unnaturally negated sentences. Balancing training data or using chain-of-thought prompting helps, but without mimicking human logical processing, scaling alone will not fully address the issue.
From the infamous example of "absolutely no elephant in the room," which was patched but resurfaces with prompts like "crocodile," to recent Agentic AI solutions, negation issues persist in lookalike domain business searches. Asking for "not Tesla" is still more likely to return Tesla-related domains, as models overlook "not" due to pattern-based limitations, leading to inaccurate results.
Check our competitors. You will not see negations, as they are just now catching up with embeddings-based lookalike search, something we had two years ago. Negations are essential when building a target account list. They are not easy, as even the best frontier models struggle with them. But we do support them at any level: domain, natural language, or keywords. Our model is a custom-built search model, not a generative model, and negations are not an issue with search.
Though "not" is frequent, models treat it syntactically, not as a logical operator, causing errors like ignoring negation, with many tests showing inaccurate or unnaturally negated sentences. Balancing training data or using chain-of-thought prompting helps, but without mimicking human logical processing, scaling alone will not fully address the issue.
From the infamous example of "absolutely no elephant in the room," which was patched but resurfaces with prompts like "crocodile," to recent Agentic AI solutions, negation issues persist in lookalike domain business searches. Asking for "not Tesla" is still more likely to return Tesla-related domains, as models overlook "not" due to pattern-based limitations, leading to inaccurate results.
Check our competitors. You will not see negations, as they are just now catching up with embeddings-based lookalike search, something we had two years ago. Negations are essential when building a target account list. They are not easy, as even the best frontier models struggle with them. But we do support them at any level: domain, natural language, or keywords. Our model is a custom-built search model, not a generative model, and negations are not an issue with search.
