LLM Evaluators

What are LLM Evaluators?

LLM-as-a-Judge Evaluators harness the reasoning power of large language models to evaluate text based on qualitative criteria, such as grammar, sentiment, tone, and factuality. Unlike Function Evaluators, they assess the context and provide human-like judgments on the quality or appropriateness of content.

Why use LLM-as-a-Judge Evaluators?

They’re perfect for scenarios where nuance matters. For example, ensuring a text’s tone aligns with your brand voice, analyzing sentiment in customer feedback, or checking the grammar of a formal email. By leveraging advanced AI, these evaluators provide insights that go beyond simple rule-based checks, helping refine and elevate your content.

Example

LLM-as-a-Judge Evaluators work like this: LLM 1 generates a response, and LLM 2 evaluates it. For instance, if LLM 1 writes an email, you can use LLM 2 to judge whether the email’s tone is professional and polite. These evaluators are like having an expert editor review your content automatically.

List of LLM evaluators

Evaluator	Description	Example
Age-Appropriate	The Age-Appropriate evaluator determines whether the generated text is appropriate for a specified age group. It is especially useful for tasks such as content moderation, educational material review, or ensuring that text is suitable for specific audiences based on age. This evaluator helps ensure that content aligns with age-appropriate language, tone, and themes.	If tasked with evaluating a summarization of news for children under 8, the Age-Appropriate evaluator checks if the language is simple, the tone is gentle, and if complex or inappropriate themes are avoided. Based on these factors, it assigns a score of 1 if the text is appropriate or 0 if it is not, flagging any necessary adjustments for improvement.
Bot detection	The Bot Detection evaluator determines whether the provided text was likely generated by an AI. It is particularly useful in tasks involving content validation, academic integrity checks, or identifying automated text in various contexts. This evaluator helps distinguish between AI-generated and human-written content based on stylistic and structural clues.	If a piece of text starts with phrases like "As an AI assistant" or shows repetitive patterns, the Bot Detection evaluator may flag it as likely AI-generated. It examines factors such as starting phrases, repetition, unnatural formality, and logical flow. Based on these characteristics, it assigns a binary score: 1 for AI-generated text and 0 for human-written, indicating whether further review is needed
Fact checking knowledge base	The Fact-Checking evaluator assesses the truthfulness of a statement by referencing an internal knowledge base and widely accepted facts. It is especially useful for tasks like verifying claims in news, public discussions, or research contexts where factual accuracy is crucial. This evaluator helps ensure statements are aligned with verified information.	If tasked with verifying the statement "Lionel Messi has won more Ballon d'Or awards than any other footballer," the Fact-Checking evaluator checks the knowledge base for sports records to confirm or refute the claim. It assigns a score according to the PolitiFact scale: from 0 (pants on fire false) to 5 (true), or -1 if uncertain, reflecting the truthfulness of the statement. Along with the score, it provides a detailed explanation referencing trusted sources to justify the evaluation.
Grammar	The Grammar evaluator checks whether the provided text is grammatically correct, focusing on grammar, punctuation, and overall clarity. This tool is especially useful for tasks like content editing, proofreading, or ensuring the accuracy of formal writing. It identifies issues that may affect readability and professionalism in written content.	Suppose the text reads, “The company are planning to expand their operations next year, its going to be a big investment for them.” The Grammar evaluator would identify errors such as subject-verb agreement ("The company are"), incorrect possessive form ("its" instead of "it’s"), and provide corrections. It assigns a binary score of 1 if the text is correct, or 0 if it contains errors, along with a corrected version when needed.
Localization	The Localization evaluator assesses the quality of localized content, focusing on accuracy, grammar, cultural appropriateness, and overall user experience. It is particularly useful for tasks involving content adaptation across languages and regions, ensuring the localized text maintains both the intended meaning and appropriateness for the target audience. This evaluation is essential for creating effective, culturally relevant content for diverse audiences.	Imagine the content "Join us for the Fourth of July sale" is localized as "7月4日のセール" for a Japanese audience. The Localization evaluator would examine whether the translation appropriately conveys the significance of the Fourth of July for a Japanese context, possibly suggesting a different approach if the cultural relevance is unclear. It assigns a score from 1 to 10, with a higher score indicating better localization quality and a detailed explanation highlighting any issues and suggestions for improvement.
PII	The PII Anonymization evaluator checks whether personally identifiable information (PII) has been correctly removed or anonymized in the output, following general anonymization guidelines. It is especially useful in contexts requiring protection of sensitive information, such as legal, healthcare, and financial data. This evaluator ensures that all identifiable details are appropriately masked to maintain privacy.	Suppose the input text contains "John Doe" and "123 Main Street," and the output replaces these with placeholders like "[NAME_1]" and "[STREET_1]." The PII Anonymization evaluator would confirm that all PII was correctly anonymized. It assigns a binary score of 1 if all PII is anonymized, or 0 if any identifying information remains, along with an explanation pointing out any missed details or errors in the anonymization process.
Sentiment classification	The Sentiment Classification evaluator checks if the sentiment of the provided text - positive, negative, or neutral - has been correctly classified based on its content. It is particularly useful for tasks like customer feedback analysis or tracking sentiment related to companies, products, or services. This evaluator helps ensure that sentiment labels align accurately with the intended tone of the text.	If the text reads, "The customer support team resolved my issue quickly and was very helpful throughout the process" and is classified as "positive," the Sentiment Classification evaluator confirms this classification. It verifies the sentiment by analyzing factors like word choice and tone, assigning a score of 1 if the classification is correct or 0 if it is not, along with an explanation detailing any discrepancies.
Summarization	The Summarization evaluator assesses the accuracy, completeness, and conciseness of a summary in relation to the original text. It is particularly useful for tasks like content summarization or abstract creation, where achieving a balance between brevity and detail is essential. This evaluator ensures that summaries capture the core points of the original content without extraneous information.	If a text discusses the recent launch of a new smartphone by a major tech company, and the summary mentions key features, launch date, and pricing, the Summarization evaluator will check whether it accurately and completely represents the original information. The summary is scored from 1 to 10, with a higher score reflecting a more precise, complete, and concise summary, accompanied by an explanation highlighting any omissions or excessive details
Tone of voice	The Tone of Voice evaluator checks whether the provided output aligns with the desired tone and writing style specified in the input. This is especially useful for tasks involving business communication, customer service, or social media, where tone consistency is crucial to conveying the right message. This evaluator ensures that responses match the intended style and tone guidelines.	Suppose the input specifies a professional and respectful tone for an email regarding a delayed payment, and the output is polite, formal, and directly addresses the issue. The Tone of Voice evaluator would confirm that the tone aligns with the guidelines, assigning a score of 1 if it meets the requirement, or 0 if it does not. The evaluation includes feedback and suggestions for improvement if the tone needs adjustment.
Translation	The Translation evaluator assesses whether the provided translation accurately conveys the meaning, tone, and style of the original text. It is especially useful for tasks like adapting marketing content, user manuals, or legal documents into different languages while maintaining the intended message and cultural nuances. This evaluator ensures that the translation is not just accurate but also culturally appropriate.	If the original text says, "The early bird catches the worm," and the Spanish translation is "El pájaro temprano atrapa el gusano," the evaluator will check if the literal translation is fitting or if a culturally relevant phrase like "A quien madruga, Dios le ayuda" would better convey the intended meaning. The evaluator assigns a score from 1 to 10 based on accuracy, completeness, and cultural adaptation, providing a detailed explanation and suggestions for improvements.