Evaluating large language models (LLMs) is a critical step in understanding their capabilities and limitations, and finding a model that best suits your organization.
As these models become more integrated into different parts of the enterprise – from customer service to data analysis – there is greater need for robust evaluation methods.
This article will explore the various methods used to evaluate LLMs, discussing their importance and the implications of their use. Effectively evaluating LLMs is essential for the advancement of AI and how it impacts the reliability of the technology we increasingly depend on every single day.
Common Evaluation Metrics
There are several techniques used to measure the performance of large language models (LLMs). These methods are designed to assess key areas such as:
- language fluency: the smoothness and naturalness of the generated text.
- coherence: the logical flow and consistency of information throughout the text.
- contextual understanding: the model’s ability to grasp and apply context in its responses.
- factual accuracy: the correctness of information and details provided by the model.
- relevant and meaningful responses: the model’s effectiveness in producing appropriate content in response to prompts.
A recent trend is instead of using the classic methods described here, practitioners take human labels and use them to train an LLM in order to predict if a response was correct or not. This can measure the understanding of the text much better than classical measures that don’t truly understand it.
Each of these areas has a role to play in the overall utility and safety of LLMs in real-world applications. Now, let’s explore some of the common metrics and methods used to evaluate these aspects:
Perplexity is a measurement of how well a probability model predicts a sample.
Measuring accuracy: Perplexity does not have an accuracy value per se. It is a measure of how well a probability distribution or probability model predicts a sample. A lower perplexity indicates the model is better at predicting the sample. For example, a perplexity of 10 is better than a perplexity of 100.
For example, if a language model is being used to generate text continuations and it often suggests highly probable and contextually appropriate continuations, it would be considered to have low perplexity. Conversely, if it frequently suggests improbable, irrelevant, or out-of-context continuations, it would be considered to have high perplexity.
Benefits and limitations:
- Provides a quantitative measure of a model’s language understanding.
- Can be calculated quickly and easily for any text.
- Does not account for the meaningfulness or relevance of the generated text.
- Lower perplexity does not necessarily mean better quality text, especially if the model is overfitted to the test data.
BLEU Score (Bilingual Evaluation Understudy)
The BLEU score is a metric for evaluating a machine’s translated text against one or more human-translated references. It measures the precision of the generated text – how many words are correct and in the correct order. While it’s widely used in machine translation, it also helps in assessing the language fluency and coherence of LLMs.
Measuring accuracy: BLEU Scores range from 0 to 1 (or 0% to 100%), where a higher score indicates better translation. A score of 0 means no overlap with the reference translation, and a score of 1 indicates perfect overlap. For instance, a BLEU score of 0.6 (or 60%) is quite good for complex tasks like machine translation.
In a real-world scenario, a high BLEU score indicates that the machine translation is very similar to the human reference, suggesting that the translation is both accurate and fluent.
Benefits and limitations:
- Easy to compute and widely used, allowing for standardization across different models.
- Good for comparing the literal translation quality of different systems.
- Can miss the assessment of the semantic accuracy and the fluency of translations.
- Relies heavily on the quality of the reference translations; if they are not of high quality, the BLEU score can be misleading.
- Does not effectively handle translations that are correct but use different wording from the reference.
ROUGE Score (Recall-Oriented Understudy for Gisting Evaluation)
ROUGE is primarily used to evaluate text summarization and machine translation. It compares an automatically generated summary or translation against a set of reference summaries, focusing on the recall – by measuring how much of the reference content appears in the generated text, whereas BLEU emphasizes precision, assessing how much of the generated text appears in the reference translations.
Measuring accuracy: ROUGE Scores also range from 0 to 1, where 1 indicates a perfect match with the reference summary. For example, a ROUGE-1 score of 0.5 means that 50% of the unigrams in the generated summary match the reference summary.
A high ROUGE score generally indicates that the generated text (like a summary or translation) has a high degree of overlap with the reference text, suggesting effectiveness in capturing the essential points.
Benefits and limitations:
- Focuses on recall, which is important for ensuring all necessary information is included in summaries.
- Can be used with multiple references to get a more balanced evaluation.
- May not fully reflect the fluency or grammatical correctness of the generated text.
- High recall can be achieved at the expense of precision, leading to verbose outputs.
METEOR Score (Metric for Evaluation of Translation with Explicit Ordering)
METEOR is another metric for evaluating machine translation. It improves upon the BLEU score by considering elements such as synonyms, and it also incorporates a measure of sentence structure into its evaluation, giving a more nuanced view of language fluency and coherence.
Measuring accuracy: METEOR Scores are also between 0 and 1, with higher scores indicating better quality translations. A METEOR score might be interpreted similarly to BLEU scores, but they are generally higher due to their more sophisticated evaluation criteria.
A high METEOR score indicates that the translation is not only accurate in terms of word-to-word translation but also captures the correct meaning, even if it uses synonyms or slightly different sentence structures. A low METEOR score may indicate that the translation, while possibly correct in a broad sense, deviates more significantly from the exact wording and structure of the reference translation.
In the example below, the machine translation, while conveying a similar overall meaning, uses different words (“felino” instead of “gato,” “descansó” instead of “se sentó,” “bajo” instead of “en,” and “alfombra” instead of “tapete”). The METEOR score would be lower due to the less direct word-to-word correspondence and the changes in prepositions and synonyms that slightly alter the meaning.
Benefits and limitations:
- Considers synonyms and paraphrasing, which can provide a more nuanced evaluation of meaning.
- Aligns more closely with human judgment than BLEU.
- More complex to compute than BLEU or ROUGE.
- Can still be influenced by the choice of reference translations.
Human evaluation remains the gold standard for assessing LLMs. It involves human judges who assess the quality of the model’s outputs based on various criteria, including the key areas mentioned above. Human evaluation can capture nuances that automated metrics might miss, providing a comprehensive understanding of a model’s performance.
Measuring accuracy: Human Evaluation does not have a standardized accuracy value. It is subjective and based on the criteria set by the evaluators. Human judges might rate the quality on a scale (e.g., 1 to 5), and these ratings can be averaged to provide a general sense of accuracy or quality.
As a reference, Tasq.ai achieves accuracy rates of over 97% using its Decentralized Human Guidance Solution.
Benefits and limitations:
- Can capture subtleties and nuances that automated metrics miss.
- Considers the overall effectiveness and appropriateness of the generated text.
- Time-consuming and expensive to conduct at scale.
- Subject to human bias and variability, which can affect consistency and reliability.
Each of these methods offers valuable insights into different facets of an LLM’s performance, and together, they provide a multifaceted view of a model’s capabilities and areas for improvement. However there is no doubt that human evaluation is the ultimate way to assess output.
Intrinsic vs. Extrinsic Evaluation
Evaluation methods can be broadly categorized into two types: intrinsic and extrinsic. These approaches assess different aspects of a language model’s performance as a part of a larger system.
Intrinsic evaluation involves assessing the performance of a language model based on the tasks it was directly trained to perform. This type of evaluation typically focuses on the intermediate steps or components of the system, such as the quality of language generation, parsing accuracy, or word embedding spaces.
Task: Analyzing the quality of word embeddings generated by a language model.
Method: One intrinsic evaluation method for this task is to use a word analogy test, where the model is asked to complete analogies like “man is to woman as king is to ?” The correct answer would be “queen.” The model’s performance on a large set of such analogies can be used to evaluate the quality of its word embeddings.
Why Intrinsic: This evaluation is intrinsic because it focuses on a fundamental aspect of the model’s language understanding—its ability to capture semantic relationships between words—without considering any end-task the embeddings might be used for.
- Specificity: It provides detailed information about the specific capabilities and limitations of a model.
- Efficiency: It can be quicker and less resource-intensive than extrinsic evaluation, as it often uses smaller, controlled datasets.
- Focused improvement: Helps in pinpointing specific areas for improvement within the model.
- Limited scope: May not reflect the model’s performance in real-world tasks or its utility in practical applications.
- Isolation from context: It doesn’t account for the interaction with other components in a larger system.
Extrinsic evaluation measures the performance of a language model based on its contribution to an external task or its effectiveness in a real-world application. This could involve tasks like machine translation, question answering, or any end-user application where the language model plays a role.
Task: Evaluating a customer service chatbot that uses a language model to interact with users.
Method: An extrinsic evaluation would involve deploying the chatbot in a live environment and measuring its performance based on specific metrics like customer satisfaction scores or resolution rate.
Why Extrinsic: This is extrinsic because it assesses the language model’s effectiveness in the context of a real-world task – customer service – rather than its performance on a language-specific test. The evaluation is based on the outcomes of the interactions, which are external to the model itself.
- Practical relevance: It assesses how well the model performs in the context of a complete system or real-world scenario.
- End-to-end testing: Provides a holistic view of the model’s utility in practical applications.
- Resource-intensive: Often requires more extensive resources to set up and conduct, as it involves complete systems or real-world tasks.
- Complexity: The performance can be influenced by many factors beyond the language model itself, making it harder to isolate the impact of the model.
Both intrinsic and extrinsic evaluations are important in the development and assessment of language models. Intrinsic evaluation is useful for understanding and improving the internal workings of a model, while extrinsic evaluation is crucial for determining the model’s ultimate value to end-users. Ideally, a combination of both is used to get a comprehensive understanding of a model’s performance and applicability.
Apart from LLM evaluation, setting up guardrails is also important. Guardrails are a set of rules or guidelines that are put in place to ensure the quality and safety of the model’s outputs. These guardrails are designed to prevent the model from generating harmful, biased, or inappropriate content.
Designing Metrics: Guardrails can include the design of specific metrics that measure not just the fluency or coherence of the model’s language, but also the fairness, bias, and toxicity of the content it generates.
Training Constraints: They may also involve constraints during the training process, such as filtering the training data to remove harmful content or using techniques like adversarial training to make the model more robust against producing undesirable outputs.
Automated Checks: Evaluation metrics can serve as automated guardrails by flagging content that scores poorly on measures of bias or toxicity. For instance, if a model’s output has a high likelihood of being toxic, the evaluation metric could automatically reject it or flag it for human review.
RLHF: Human evaluation acts as a guardrail by providing a check against the model’s outputs. Human evaluators can assess nuances in language that automated metrics might miss, such as subtle forms of bias or the appropriateness of content in a specific cultural context. Humans can also look out for potential brand damage.
Monitoring: After deployment, guardrails include continuous monitoring of the model’s performance to quickly identify and address any issues with the outputs in real-world scenarios.
Feedback Loops: Establishing feedback loops where users can report problematic outputs helps maintain the quality and safety of the model. These reports can be used to fine-tune the model and its evaluation metrics.
Recent Advances in LLM Evaluation
There have been some significant advances in LLM evaluation of late, from multidimensional evaluation frameworks that go beyond traditional metrics like BLEU or ROUGE, to new benchmarks like GLUE and SuperGLUE, and increasing use of cross-lingual and cross-cultural evaluation.
One key trend is the increasingly central role played by human-centered evaluation. Human-centered evaluation approaches have gained traction, recognizing that human judgment is the ultimate benchmark for many applications of LLMs.
In this context, Tasq.ai’s crowd-based Decentralized Human Guidance solution provides all the benefits of technology – scalability, flexibility, accuracy and seamlessness – with the unmatched effectiveness of human guidance across multiple geos.
Customers get automatic flows with a guaranteed SLA, and the solution is perfect for a quick evaluation during training, before deployment, and offers constant automatic monitoring.
We’ve seen the importance of robust LLM model evaluation, including common evaluation methods like Perplexity, BLEU, ROUGE and METEOR, alongside the indispensable role of human evaluation.
As you develop your models, you’ll find the right combination of LLM evaluation methods that work for you.