For long, humans have been deployed to execute some of the most redundant tasks in the name of processes and workflows. This dedication of human power to perform monotonous jobs has resulted in reduced utilization of abilities and resources into resolving concerns that actually demand human capabilities.
However, with the onset of Artificial Intelligence (AI), specifically Gen AI and its allied technologies such as Large Language Models (LLMs), we have successfully automated redundant tasks. This has paved the way for humans to refine their skills and take up niche responsibilities that have actual real-world impact.
Simultaneously, enterprises have uncovered newer potential for AI in the form of use cases and applications in diverse streams, increasingly relying on them for insights, actionable, conflict resolutions, and even outcome predictions. Statistics also reveal that by 2025, over 750mn apps will be powered by LLMs.
As LLMs gain increased prominence, it’s on us tech experts and tech enterprises to unlock level 2, which is grounded on responsible and ethical AI aspects. With LLMs influencing decisions in sensitive domains such as healthcare, legal, supply-chain and more, the mandate for foolproof and airtight models becomes inevitable.
So, how do we ensure LLMs are trustworthy? How do we add a layer of credibility and accountability while developing LLMs?
LLM evaluation is the answer. In this article, we will anecdotally break down what LLM evaluation is, some LLM evaluation metrics, its importance, and more.
Let’s get started.
What Is LLM Evaluation?
In the simplest of words, LLM evaluation is the process of assessing the functionality of an LLM in aspects surrounding:
- Accuracy
- Efficiency
- Trust
- And safety
The assessment of an LLM serves as a testimony to its performance and gives developers and stakeholders a clear understanding of its strengths, limitations, scope of improvement, and more. Such evaluation practices also ensure LLM projects are consistently optimized and calibrated so they are perpetually aligned with business goals and intended outcomes.
Why Do We Need To Evaluate LLMs?
LLMs like GPT 4.o, Gemini and more are becoming increasingly integral in our everyday lives. Apart from consumer aspects, enterprises are customizing and adopting LLMs to execute a myriad of their organizational tasks through deployment of chatbots, in healthcare to automate appointment scheduling, in logistics for fleet management and more.
As the dependence on LLMs increases, it becomes crucial for such models to generate responses that are accurate and contextual. The process of LLM evaluation boils down to factors such as:
- Improving the functionality and performance of LLMs and strengthening their credibility
- Enhancing safety by ensuring mitigation of bias and the generation of harmful and hateful responses
- Meeting the needs of users so they are capable of generating human-like responses in situations both casual and critical
- Identifying gaps in terms of areas a model needs improvement
- Optimizing domain adaptation for seamless industry integration
- Testing multilingual support and more
Applications Of LLM Performance Evaluation
LLMs are critical deployments in enterprises. Even as a tool for a consumer, LLMs have serious implications in decision-making.
That’s why rigorously evaluating them goes beyond an academic exercise. It’s a stringent process that needs to be inculcated at a culture level to ensure negative consequences are at bay.
To give you a quick glimpse of why LLM evaluations are important, here are a few reasons:
Assess Performance
LLM performance is something that is consistently optimized even after deployment. Their assessments give a bird’s eye view on how they understand human language and input, how they precisely process requirements, and their retrieval of relevant information.
This is extensively done by incorporating diverse metrics that are aligned with LLM and business goals.
Identify & Mitigate Bias
LLM evaluations play a crucial role in detecting and eliminating bias from models. During the model training phase, bias through training datasets are introduced. Such datasets often result in one-sided results that are innately prejudiced. And enterprises cannot afford to launch LLMs loaded with bias. To consistently remove bias from systems, evaluations are conducted to make the model more objective and ethical.
Ground Truth Evaluation
This method analyzes and compares results generated by LLMS with actual facts and outcomes. By labeling outcomes, results are weighed in against their accuracy and relevance. This application enables developers to understand the strengths and limitations of the model, allowing them to further take corrective measures and optimization techniques.
Model Comparison
Enterprise-level integrations of LLMs involve diverse factors such as the domain proficiency of the model, the datasets its trained on and more. During the objective research phase, LLMs are evaluated based on their models to help stakeholders understand which model would offer the best and precise results for their line of business.
LLM Evaluation Frameworks
There are diverse frameworks and metrics available to assess the functionality of LLMs. However, there is no rule of thumb to implement and the preference to an LLM evaluation framework boils down to specific project requirements and goals. Without getting too technical, let’s understand some common frameworks.
Context-specific Evaluation
This framework weighs the domain or business context of an enterprise and its overarching purpose against the functionality of the LLM being built. This approach ensures responses, tone, language, and other aspects of output are tailored for context and relevance and that there are no appropriations to avoid reputational damage.
For instance, an LLM designed to be deployed in schools or academic institutions will be evaluated for language, bias, misinformation, toxicity, and more. On the other hand an LLM being deployed as a chatbot for an eCommerce store will be evaluated for text analysis, accuracy of output generated, ability to resolve conflicts in minimal conversation and more.
For better understanding, here’s a list of evaluation metrics ideal for context-specific evaluation:
| Relevance | Does the model’s response align with a user’s prompt/query? |
| Question-answer accuracy | This evaluates a model’s ability to generate responses to direct and straightforward prompts. |
| BLEU score | Abbreviated as Bilingual Evaluation Understudy, this assesses a model’s output and human references to see how closely the responses are to that of a human. |
| Toxicity | This checks if the responses are fair and clean, devoid of harmful or hateful content. |
| ROGUE Score | ROGUE stands for Recall-oriented Understudy For Gisting Evaluation and understands the ratio of the reference content to its generated summary. |
| Hallucination | How accurate and factually right is a response generated by the model? Does the model hallucinate illogical or bizarre responses? |
User-driven Evaluation
Regarded as the gold standard of evaluations, this involves the presence of a human in scrutinizing LLM performances. While this is incredible to understand the intricacies involved in prompts and outcomes, it is often time-consuming specifically when it comes to large-scale ambitions.
UI/UX Metrics
There’s the standard performance of an LLM on one side and there’s user experience on the other. Both have stark differences when it comes to choosing evaluation metrics. To kickstart the process, you can consider factors such as:
- User satisfaction: How does a user feel when using an LLM? Do they get frustrated when their prompts are misunderstood?
- Response Time: Do users feel the model takes too much time to generate a response? How satisfied are users with the functionality, speed, and accuracy of a particular model?
- Error recovery: Mistakes happen but effectively does a model rectify its mistake and generate an appropriate response? Does it retain its credibility and trust by generating ideal responses?
User experience metrics sets an LLM evaluation benchmark in these aspects, giving developers insights on how to optimize them for performance.
Benchmark Tasks
One of the other prominent frameworks includes assessments such as MT Bench, AlpacaEval, MMMU, GAIA and more. These frameworks comprise sets of standardized questions and responses to gauge the performance of models. One of the major differences between the other approaches and this is that they are generic frameworks that are ideal for objective analysis of LLMs. They function over generic datasets and may not provide crucial insights for the functionality of models with respect to specific domains, intentions, or purpose.
LLM Model Evaluation Vs. LLM System Evaluationz
Let’s go a little more in-depth in understanding the different types of LLM evaluation techniques. By becoming familiar with an overarching spectrum of evaluation methodologies, developers and stakeholders are in a better position to evaluate models better and contextually align their goals and outcomes.
Apart from LLM model evaluation, there is a distinct concept called LLM system evaluation. While the former helps gauge a model’s objective performance and capabilities, LLM system evaluation assesses a model’s performance in a specific context, setting, or framework. This lays emphasis on a model’s domain and real-world application and a user’s interaction surrounding it.
| Model Evaluation | System Evaluation |
| It focuses on the performance and functionality of a model. | It focuses on the effectiveness of a model with respect to its specific use case. |
| Generic, all encompassing evaluation across diverse scenarios and metrics | Prompt engineering and optimization to enhance user experience |
| Incorporation of metrics such as coherence, complexity, MMLU and more | Incorporation of metrics such as recall, precision, system-specific success rates, and more |
| Evaluation outcomes directly influence foundational development | Evaluation outcomes influences and enhances user satisfaction and interaction |
Understanding The Differences Between Online And Offline Evaluations
LLMs can be evaluated both online and offline. Each offers its own set of pros and cons and is ideal for specific requirements. To understand this further, let’s break down the differences.
| Online Evaluation | Offline Evaluation |
| The evaluation happens between LLMs and real user-fed data. | This is conducted in a conscious integration environment against existing datasets. |
| This captures the performance of an LLM live and gauges user satisfaction and feedback in real time. | This ensures performance meets basic functioning criteria eligible for the model to be taken live. |
| This is ideal as a post-launch exercise, further optimizing LLM performance for enhanced user experience. | This is ideal as a pre-launch exercise, making the model market-ready. |
LLM Evaluation Best Practices
While the process of evaluating LLMs is complex, a systematic approach can make it seamless from both business operations and LLM functionalities aspects. Let’s look at some best practices to evaluate LLMs.
Incorporate LLMOps
Philosophically, LLMOps is similar to DevOps, focussing predominantly on automation, continuous development, and increased collaboration. The difference here is that LLMOps substantiates collaboration among data scientists, operations teams, and machine learning developers.
Besides, it also aids in automating machine learning pipelines and has frameworks to consistently monitor model performance for feedback and optimization. The entire incorporation of LLMOps ensures your models are scalable, agile, and reliable apart from ensuring they are compliant to mandates and regulatory frameworks.
Maximum Real-world Evaluation
One of the time-tested ways to implement an airtight LLM evaluation process is to conduct as many real-world assessments as possible. While evaluations in controlled environments are good to gauge model stability and functionality, the litmus test lies when models interact with humans on the other side. They are prone to unexpected and bizarre scenarios, compelling them to learn new response techniques and mechanisms.
An Arsenal Of Evaluation Metrics
A monolithic approach to featuring evaluation metrics only brings in a tunnel-vision syndrome to model performances. For a more holistic view that offers an all-encompassing view of LLM performance, it’s suggested you have a diverse analysis metric.
This should be as broad and exhaustive as possible including coherence, fluency, precision, relevance, contextual comprehension, time taken for retrieval, and more. The more the assessment touchpoints, the better the optimization.
Critical Benchmarking Measures To Optimize LLM Performance
Benchmarking of a model is essential to ensure refinement and optimization processes are kickstarted. To pave the way for a seamless benchmarking process, a systematic and structured approach is required. Here, we identify a 5-step process that will help you accomplish this.
- Curation of benchmark tasks that involves diverse simple and complex tasks so benchmarking happens across the spectrum of an model’s complexities and capabilities
- Dataset preparation, featuring bias-free and unique datasets to assess a model’s performance
- Incorporation of LLM gateway and fine-tuning processes to ensure LLMs seamlessly tackle language tasks
- Assessments using the right metrics to objectively approach the benchmarking process and lay a solid foundation for the model’s functionality
- Result analysis and iterative feedback, triggering a loop of inference-optimization process for further refinement of model performance
The completion of this 5-step process will give you a holistic understanding of your LLM and its functionality through diverse scenarios and metrics. As a summary of the performance evaluation metrics used, here’s a quick table:
| Metric | Purpose | Use Case |
| Perplexity | To measure any uncertainty in predicting next tokens | Language proficiency |
| ROGUE | To compare reference text and a model’s output | Summarization-specific tasks |
| Diversity | To evaluate the variety of outputs generated | Variation and creativity in responses |
| Human Evaluation | To have humans in the loop to determine subjective understanding and experience with a model | Coherence and relevance |
LLM Evaluation: A Complex Yet Indispensable Process
Assessing LLMs is highly technical and complex. With that said, it’s also a process that cannot be skipped considering its cruciality. For the best way forward, enterprises can mix and match LLM evaluation frameworks to strike a balance between assessing the relative functionality of their models to optimizing them for domain integration in the GTM (Go To Market) phase.
Apart from their functionality, LLM evaluation is also critical to increment confidence in AI systems enterprises build. As Shaip is an advocate of ethical and responsible AI strategies and approaches, we always vouch and voice for stringent assessment tactics.
We truly believe this article introduced you to the concept of evaluation of LLMs and that you have a better idea of how it’s crucial for safe and secure innovation and AI advancement.
