ChatGPT is a widely popular chat interface for everyone now for all the right reasons.

It can generate new texts, translate multiple languages, perform NLP tasks, and reduce human workloads by lowering what is repetitive for humans.

But, what unexpectedly gained a lot of traction for ChatGPT is the underlying technology— LLM or large language model.

LLMs are a new attraction for enterprise leaders to drive massive AI transformation to elevate the pace of efficiency and productivity.

However, ethical concerns can arise, as seen with the existing off-the-shelf platforms, such as pre-trained models, ChatGPT, Gemini, etc.

LLMs with data with a specific threshold period can lack the current world of knowledge, limiting specificity and causing hallucinations.

So, applications built over off-the-shelf LLM models can be risky.

Simultaneously, new large language models built with new data can also hallucinate and produce biased behavior.

LLM evaluation is highly significant in ensuring trust and credibility.

Evaluating LLMs is overwhelming work and should encompass multiple assessment procedures.

It is essential to find the best ways to evaluate large language models and harness the correct data set for model training and assessment.

However, how can we assess LLM for enterprise? Let’s know the best ways we can evaluate large language models.

LLM evaluation, known as Evals in short form, is a comprehensive process to assess the capabilities and functionalities of large language models.

LLM evals can also be considered as metrics or measurements to assess the performance of LLMs or LLM-built applications.

Model evaluation primarily helps understand the product-readiness of an LLM application or model.

An evaluation process is effective in identifying loopholes rather than weaknesses. A robust LLM evaluation process helps you assess how your application can interact with end users in real-world scenarios and offer the best user experience.

Industry leaders are aware of the biases or other threats of LLMs.

LLM evals make it easy to flag anything that might show negative behavior. As a result, LLM evaluation can help us improve model performance and continue to produce a system to works to its best capacity.

To further highlight how an LLM eval can look, we can explain this way,

• Evals ensure LLM works well. Evals are like grading systems to evaluate whether an LLM learns to respond well and understand mistakes.

• LLM evals produce a better model. The continuous large language model evaluation can help us reveal where a model is struggling and provide data-driven suggestions to teach LLMs to act smarter.

• Evals can prevent safety concerns. A robust assessment process can help us identify how a model can go wrong, fix it, and use it for better use cases they are good at.

LLMs allow multiple use cases depending on their text generation and language understanding.

One of the leading use cases is AI chatbots, which simplify language processing and understanding for better delivery of responses and problem resolution.

Within this context, if a conversational AI application does not integrate an LLM model evaluated comprehensively for its varied use cases, it erroneously impact user experiences by delivering wrong responses or exhibiting biased behavior.

It is essential to comprehensively check LLM applications or models before being put to use.

LLM evaluation must encompass assessment metrics to ensure that models or applications deliver expected services without a minor glitch. This is essentially important if you want to implement LLM models for your customer service use cases or internal employee support.

Let us know what you need to consider—

Model accuracy

LLM evaluation must confirm that the produced outcome is accurate in accordance with query inputs.

It means The LLM model must have the ability to understand inputs and deliver answers accurately.

For example, if a customer raises a query for refund status, an LLM-powered chat interface must understand the user’s query and provide an accurate answer. So, LLM evaluation helps assess every answer against a set of questions or domain-specific scenarios.

With that, LLM evaluation can also help detect inaccuracies, put them back to correction, and ensure accuracy.

Model fluency

This is a significant criterion for LLM evaluation that ensures an LLM model can comprehend proper grammatical aspects—sentence construction, spelling correctness, syntax understanding, etc.

This capability helps LLM models generate grammatically correct texts to improve answer acceptance and encourage users to seek help from a Generative AI-based chat platform.

In this scenario, we can also assume that if a user sends queries in a fragmented way, an LLM model can still recognize the query and provide correct responses by following the fluency of language.

LLM model precision and relevance

The evaluation method for large language models must ensure response precision and consistency.

A query can mean varied contexts. LLM evaluation must ensure that LLM models or applications can understand the context a user wants to convey and provide a relevant and consistent answer.

For example, if a user asks, ‘How do I reset my password’? This question can involve any type of device requiring password reset help.

An LLM model must follow predefined steps to help users in this scenario.

There can be straightforward instructions from a chatbot if a user needs help with an enterprise productivity app. On the other hand, if it is about resetting passwords for the corporate network, an LLM app can ask to connect with the human agent at the help desk or IT department.

LLM model consistency

Consistent responses are highly desirable with LLM-powered chatbots across customer or employee support.

If a model generates inconsistent responses all across its generated texts, it can impact problem-solving and confusion and increase workloads for human agents.

The evaluation criterion for LLM must involve assessing the consistency level of any LLM model to improve user experience.

Say a user asks a travel assistant bot a query about interesting sightseeing in Paris. LLM evals must ensure that it provides responses around destinations and key attractions only in Paris rather than generating answers about other cities in France.

Also, if a user wants to verify a question by providing a wrong response, the LLM model must correct the user in the follow-up question and maintain consistency.

Scalability and performance

Ticket influx can be a usual scenario across the customer or employee support ecosystem in a holiday or festive season, where users want answers for shopping issues. On the other way around, IT must work in tandem to provide excellent uptime for devices and applications.

Both these scenarios demand an LLM model to handle the bandwidth of questions and deliver responses quickly.

An LLM evaluation process also includes assessing an LLM model's scalability and performance capability so that users can easily have seamless response and problem-solving flexibility.

Adaptability to specific enterprise needs and terminology

Generative AI-powered conversational AI can easily adapt to any business function and industry.

However, the problem is that every industry has different terminology that is significant for users as they ask questions. If models lack knowledge about these terminologies, they aren’t useful for industry-specific use cases.

As part of your LLM evaluation process, it is essential to ensure that LLM models follow proper training datasets that explain industry-specific terminology and facilitate response delivery.

Compatibility with existing IT infrastructure and systems

Industry leaders aim to streamline business process workflows, gain efficiency, and maximize productivity.

Generative AI applications must provide API integration to allow existing IT systems, such as CRM, ERP, ITSM, or service desk platforms, to automate workflows.

It is a critical part of the LLM evaluation job to ensure that LLM applications are perfectly compatible with existing systems, help communicate with each other, and facilitate automation of desired workflows.

Security and bias detection and mitigation

Bias detection is part of LLM evaluation, which is critical to identifying the tendencies of models to produce prejudiced responses and create bias.

For example, if a particular training data includes conversations or messages that showcase banter or hatred toward a specific community or gender, a response or outcome can be biased.

So, LLM evaluation must carefully flag these propensities in training data to improve veracity.

Similarly, it is also essential to observe that LLM applications give accurate and relevant information rather than generating misinformation, false or misleading information that can cause security threats.

Compliance with industry regulations and standards

GDPR, FTC, and other regulatory bodies provide details about maintaining compliance with industry standards and preventing security risks.

LLM evaluations must follow strict criteria that adhere to these regulatory mechanisms.

Let’s understand that one-size-fits-all cannot be appropriate for LLM evaluations.

Depending on the various use cases, evaluations must encompass custom requirements or unique demands of specific scenarios to evaluate the performance and veracity of an LLM model.

For example, if an LLM model is built for the medical domain, it must follow evaluation metrics related to the clinical ecosystem.

Response relevance allows patients to have clear conversations. Accuracy in question and answer is vital for patients to derive appropriate answers regarding any disease and illness.

On the other hand, if a model has to take care of the service desk, the evaluation process includes metrics for service desk cases such as password reset, account unlock, etc.

Evaluating large language models is necessary for every industry leader to consider. LLM evaluations are essential for gauging performance, capabilities, and weaknesses.

Executing these evals can help you prevent risks to the models and ensure model veracity for maximum user experience.

However, LLM evals can look challenging depending on various use cases. We’ve put up a list of considerations for LLM evaluation. They can be effective for your specific use case and allow you to prevent business risks.

With that, if you want to get started in no time and experience LLM transformation for your service desk or ITSM, book a demo with Workativ.