27 Mar

Striving for Certainty: Navigating the Landscape of Language Model Testing

Brett King

TLDR: A discussion on testing Language Models (LLMs) in AI, covering historical context, key evaluation frameworks, and transparency issues. It emphasises challenges in applying LLM metrics to real-world business decisions and offers some advice for navigating these complexities as we deploy LLMs to support the reimagining of our business processes.


As a Product Consultant, uncertainty is a close companion and I have a restless desire to qualify and quantify. It’s a superpower. A passionate desire. And, at times, my kryptonite. 

Nowhere has it been more towards the latter than in working with LLM-based solutions when the time given to demonstrate value is, at best, lean. Couple that with an increasing demand for certainty and explainability as the world of technology has become more general and probabilistic, and you have a challenging environment to operate in. 

Fear not. My relentless desire for clarification has led me on a journey of discovery and insight. One that is feeding my superpower nicely.  So, let me share some of it with you. 

The Technical Part

The Business Part

Before We Start: Horses for courses

Language Models (LLMs) come in various forms. For instance, OpenAI's GPT series (e.g., GPT-3, GPT-4) excel in natural language understanding and generation, while BERT (Bidirectional Encoder Representations from Transformers) by Google is renowned for its prowess in natural language processing tasks like sentiment analysis and classification. 

Testing these models involves assessing their ability to comprehend, generate, and manipulate human-like language, often through a series of standardised evaluation tasks.

Believe it or not, this is not a recent path. LLM testing traces back to the 1990s, when neural network-based models started gaining prominence in natural language processing. As neural network models emerged, the need for more sophisticated evaluation techniques, standardised evaluation frameworks and benchmarks became evident.

The Technical Part

The Key Language Testing Frameworks

As LLMs evolve so does the need for more appropriate testing frameworks to validate and benchmark their “performance”. 

Here is a brief timeline of the key language oriented frameworks (not included here are the frameworks designed for non-linguistic tests):

Framework Released Strengths Weaknesses
BLEU 2002 Simple and easy to use Limited in capturing nuances
ROUGE 2004 Effective for text summarisation tasks Less effective for other tasks
GLUE 2018 Comprehensive suite of tasks May lack domain-specific tasks
SuperGlue 2019 More complex tasks for better evaluation Complexity can be a drawback
BBH 2021 Incorporates human evaluation Subjectivity in human assessment
Big-Bench 2022 Benchmark for large-scale LLMs Scalability challenges
MMLU 2023 Holistic assessment across tasks Complexity in interpretation

Differences in Task Approaches

Each evaluation framework encompasses a diverse set of tasks, testing different aspects of LLM capabilities:

  • BLEU and ROUGE: Emphasise translation fidelity and summary coherence.
  • GLUE and SuperGlue: Cover sentiment analysis, classification, and coreference resolution, evaluating LLM comprehension and reasoning abilities.
  • BBH: Incorporates human evaluators to assess comprehension and context.
  • Big-Bench and MMLU: Present a wide range of tasks, testing LLM generalisation and consistency.

All of these later benchmarks are looking to grade LLMs in ways not dissimilar to how humans are graded with standardised tests. Imagine your LLMs as a graduating class seeking a placement on your graduate program. What skills are you really looking for in your graduate?

Evaluation Approaches 

Alongside the framework itself, a model vendor or leaderboard tester can define additional parameters defined as approaches. 

Evaluation approaches can offer unique insights into different aspects of LLM performance, helping researchers and practitioners understand the strengths and weaknesses of language models across various tasks and datasets. 

By employing a combination of these approaches, a more comprehensive assessment of LLM capabilities can be achieved.

  • Zero-Shot Evaluation: In zero-shot evaluation, LLMs are tested on tasks for which they have not been explicitly exposed to. This approach assesses the model's ability to generalise to unseen tasks and demonstrates its understanding of underlying linguistic patterns. Zero-shot evaluation helps evaluate the adaptability and generalisation capabilities of LLMs.
  • Few-Shot Evaluation: Few-shot evaluation involves familiarising the LLMs with a number of examples (few shots) for a specific task and evaluating their performance on similar but unseen examples. This approach tests the model's ability to learn from sparse data and adapt quickly to new tasks, making it particularly useful for scenarios with limited training data.
  • One-Shot Evaluation: Similar to few-shot evaluation, one-shot evaluation assesses LLM performance exposure to  a single example (one shot) of a particular task. This approach evaluates the model's ability to generalise from minimal input and make accurate predictions with limited training instances.
  • Maj1 Approach: In this approach, the model's predictions are compared to the majority class in the dataset. It highlights the most prevalent outcomes and is commonly used in classification tasks where one class dominates the dataset.

These vendor/tester-led evaluation approaches can also hamper the ability to compare across models as vendors utilise differing approaches to garner a competitive score. 

Addressing Transparency Issues in LLM Testing

Further complicating this landscape is the transparency issues in LLM testing. 

The transparency issue in LLM (Language Model) testing refers to the lack of clarity and openness surrounding the evaluation process and the datasets used for testing language models. This issue arises due to several factors:

Opaque Evaluation Metrics: Some evaluation metrics used in LLM testing may not fully capture the nuances of language understanding and generation. For example, traditional metrics like BLEU and ROUGE focus on surface-level similarities between model outputs and reference texts, often overlooking semantic coherence and contextual appropriateness.

Limited Dataset Disclosure: In many cases, the datasets used for evaluating LLMs are not fully disclosed or documented, making it challenging to assess the representativeness and diversity of the data. Without access to the underlying datasets, researchers and practitioners cannot verify the quality, relevance, or potential biases present in the evaluation data. This is not the case for some of the most recent testing frameworks. 

Data Leakage: This is a key challenge to transparency in LLM evaluation and occurs when information from the test set inadvertently influences the training process of the language model. This can lead to inflated performance scores, as the model may unintentionally memorise patterns or examples from the evaluation data rather than learning generalizable linguistic features.

Bias and Fairness Concerns: Language models trained on biassed or unrepresentative datasets can perpetuate and amplify societal biases present in the training data. Without transparency regarding dataset composition and evaluation methodologies, it is difficult to identify and mitigate biases, leading to potential ethical and societal implications.

Algorithmic Complexity: The inner workings of modern LLMs, such as transformer-based architectures like GPT (Generative Pre-trained Transformer), are highly complex and opaque. Understanding how these models make predictions and generate language outputs can be challenging, further complicating the evaluation process and limiting transparency.

Lack of Standardisation: The absence of standardised evaluation protocols and benchmarks makes it challenging to compare the performance of different LLMs accurately. Without clear guidelines and benchmarks, it becomes difficult to assess model improvements over time and across different research studies or commercial implementations.

Addressing the transparency issue in LLM testing requires concerted efforts from researchers, practitioners, and policymakers. Greater transparency in evaluation metrics, dataset documentation, and model architectures can enhance accountability, reproducibility, and trust in LLM research and applications. Additionally, promoting open access to evaluation datasets and fostering collaboration between stakeholders can facilitate more robust and comprehensive evaluation practices in the field of natural language processing.

The Business Part

Challenges in Business Decision-Making Using LLM Metrics Alone

It is often a struggle to translate LLM performance metrics based on research benchmarking into actionable insights for real-world applications. To inform early decision making in the solution space requires a nuanced understanding and testing of both LLM capabilities and specific business opportunity or outcome, combined with experience at scale. 

Integrating qualitative analysis, domain expertise, and iterative refinement processes can enhance the relevance and applicability of LLM performance data in business decision-making.

The landscape of LLM testing has undergone significant evolution, driven by advancements in AI technology and the growing demand for robust evaluation methodologies. By addressing transparency issues and bridging the gap between testing metrics and real-world applications, businesses can harness the full potential of LLMs for their specific needs.

At Mesh-AI we are uniquely positioned to explore the intersection of research metrics and real world applications, and are exploring them to support our customers. 

Key Learnings

When beginning your journey, leverage the experience of people who are driving deployments of LLM-based solutions now. Experience is King at this stage if you wish to ensure accelerated return on investment. 

Allow time for experimentation. This does not need to be onerous but it must be a key consideration when competing alternatives exist. Ensure it is combined with a rigorous approach to validation that aligns to your desired business outcomes. Measure twice. Cut Once. 

Size isn't everything. Whilst the biggest and best may seem the easier path, this can be problematic in the long run. With parameter sizes skyrocketing at every iteration it is easy to believe the contrary. There is a lot of benefit to exploring smaller models for domain specific opportunities where the investment may be required in the development stage but the cost of ownership will undoubtedly be reduced in the long run. [See Allow time for experimentation)

Finally, if you are looking to explore early value indicators that are applicable to your business opportunities then it is safe to say you have not yet built a team to support your adventures internally. That’s where Mesh-AI comes in. 

We’re working with customers right now and deploying across multiple verticals and technology estates, from which we are continuing to build insights that help bridge this gap between LLM metric performance as published by the vendor and the opportunities that businesses want to explore.

Personally, I will continue my relentless journey to bridge the gap between vendor metrics and business value where LLMs and AI is concerned. There is always something new and exciting but the most important thing is building value for customers. 

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