Large language models (LLMs) like GPT-4, Claude, and LLaMA have exploded in popularity. Because of their ability to generate impressively human-like text, these AI systems are actually getting used for all the things from content creation to customer support chatbots.
But how can we know if these models are literally any good? With recent LLMs being announced continually, all claiming to be larger and higher, how can we evaluate and compare their performance?
On this comprehensive guide, we’ll explore the highest techniques for evaluating large language models. We’ll have a look at the professionals and cons of every approach, after they are best applied, and the way you possibly can leverage them in your personal LLM testing.
Task-Specific Metrics
Probably the most straightforward ways to guage an LLM is to check it on established NLP tasks using standardized metrics. For instance:
Summarization
For summarization tasks, metrics like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are commonly used. ROUGE compares the model-generated summary to a human-written “reference” summary, counting the overlap of words or phrases.
There are several flavors of ROUGE, each with their very own pros and cons:
- ROUGE-N: Compares overlap of n-grams (sequences of N words). ROUGE-1 uses unigrams (single words), ROUGE-2 uses bigrams, etc. The advantage is it captures word order, but it may possibly be too strict.
- ROUGE-L: Based on longest common subsequence (LCS). More flexible on word order but focuses on foremost points.
- ROUGE-W: Weights LCS matches by their significance. Attempts to enhance on ROUGE-L.
Basically, ROUGE metrics are fast, automatic, and work well for rating system summaries. Nonetheless, they do not measure coherence or meaning. A summary could get a high ROUGE rating and still be nonsensical.
The formula for ROUGE-N is:
ROUGE-N=∑∈{Reference Summaries}∑∑�∈{Reference Summaries}∑
Where:
Count_{match}(gram_n)is the count of n-grams in each the generated and reference summary.Count(gram_n)is the count of n-grams within the reference summary.
For instance, for ROUGE-1 (unigrams):
- Generated summary: “The cat sat.”
- Reference summary: “The cat sat on the mat.”
- Overlapping unigrams: “The”, “cat”, “sat”
- ROUGE-1 rating = 3/5 = 0.6
ROUGE-L uses the longest common subsequence (LCS). It’s more flexible with word order. The formula is:
ROUGE-L=���(generated,reference)max(length(generated), length(reference))
Where LCS is the length of the longest common subsequence.
ROUGE-W weights the LCS matches. It considers the importance of every match within the LCS.
Translation
For machine translation tasks, BLEU (Bilingual Evaluation Understudy) is a well-liked metric. BLEU measures the similarity between the model’s output translation and skilled human translations, using n-gram precision and a brevity penalty.
Key points of how BLEU works:
- Compares overlaps of n-grams for n as much as 4 (unigrams, bigrams, trigrams, 4-grams).
- Calculates a geometrical mean of the n-gram precisions.
- Applies a brevity penalty if translation is way shorter than reference.
- Generally ranges from 0 to 1, with 1 being perfect match to reference.
BLEU correlates reasonably well with human judgments of translation quality. However it still has limitations:
- Only measures precision against references, not recall or F1.
- Struggles with creative translations using different wording.
- At risk of “gaming” with translation tricks.
Other translation metrics like METEOR and TER try and improve on BLEU’s weaknesses. But basically, automatic metrics don’t fully capture translation quality.
Other Tasks
Along with summarization and translation, metrics like F1, accuracy, MSE, and more could be used to guage LLM performance on tasks like:
- Text classification
- Information extraction
- Query answering
- Sentiment evaluation
- Grammatical error detection
The advantage of task-specific metrics is that analysis could be fully automated using standardized datasets like SQuAD for QA and GLUE benchmark for a spread of tasks. Results can easily be tracked over time as models improve.
Nonetheless, these metrics are narrowly focused and might’t measure overall language quality. LLMs that perform well on metrics for a single task may fail at generating coherent, logical, helpful text basically.
Research Benchmarks
A well-liked solution to evaluate LLMs is to check them against wide-ranging research benchmarks covering diverse topics and skills. These benchmarks allow models to be rapidly tested at scale.
Some well-known benchmarks include:
- SuperGLUE – Difficult set of 11 diverse language tasks.
- GLUE – Collection of 9 sentence understanding tasks. Simpler than SuperGLUE.
- MMLU – 57 different STEM, social sciences, and humanities tasks. Tests knowledge and reasoning ability.
- Winograd Schema Challenge – Pronoun resolution problems requiring common sense reasoning.
- ARC – Difficult natural language reasoning tasks.
- Hellaswag – Common sense reasoning about situations.
- PIQA – Physics questions requiring diagrams.
By evaluating on benchmarks like these, researchers can quickly test models on their ability to perform math, logic, reasoning, coding, common sense, and far more. The share of questions accurately answered becomes a benchmark metric for comparing models.
Nonetheless, a serious issue with benchmarks is training data contamination. Many benchmarks contain examples that were already seen by models during pre-training. This allows models to “memorize” answers to specific questions and perform higher than their true capabilities.
Attempts are made to “decontaminate” benchmarks by removing overlapping examples. But that is difficult to do comprehensively, especially when models could have seen paraphrased or translated versions of questions.
So while benchmarks can test a broad set of skills efficiently, they can not reliably measure true reasoning abilities or avoid rating inflation resulting from contamination. Complementary evaluation methods are needed.
LLM Self-Evaluation
An intriguing approach is to have an LLM evaluate one other LLM’s outputs. The thought is to leverage the “easier” task concept:
- Producing a high-quality output could also be difficult for an LLM.
- But determining if a given output is high-quality could be a better task.
For instance, while an LLM may struggle to generate a factual, coherent paragraph from scratch, it may possibly more easily judge if a given paragraph makes logical sense and matches the context.
So the method is:
- Pass input prompt to first LLM to generate output.
- Pass input prompt + generated output to second “evaluator” LLM.
- Ask evaluator LLM an issue to evaluate output quality. e.g. “Does the above response make logical sense?”
This approach is fast to implement and automates LLM evaluation. But there are some challenges:
- Performance depends heavily on alternative of evaluator LLM and prompt wording.
- Constrainted by difficulty of original task. Evaluating complex reasoning remains to be hard for LLMs.
- May be computationally expensive if using API-based LLMs.
Self-evaluation is particularly promising for assessing retrieved information in RAG (retrieval-augmented generation) systems. Additional LLM queries can validate if retrieved context is used appropriately.
Overall, self-evaluation shows potential but requires care in implementation. It complements, relatively than replaces, human evaluation.
Human Evaluation
Given the constraints of automated metrics and benchmarks, human evaluation remains to be the gold standard for rigorously assessing LLM quality.
Experts can provide detailed qualitative assessments on:
- Accuracy and factual correctness
- Logic, reasoning, and customary sense
- Coherence, consistency and readability
- Appropriateness of tone, style and voice
- Grammaticality and fluency
- Creativity and nuance
To judge a model, humans are given a set of input prompts and the LLM-generated responses. They assess the standard of responses, often using rating scales and rubrics.
The downside is that manual human evaluation is dear, slow, and difficult to scale. It also requires developing standardized criteria and training raters to use them consistently.
Some researchers have explored creative ways to crowdfund human LLM evaluations using tournament-style systems where people bet on and judge matchups between models. But coverage remains to be limited in comparison with full manual evaluations.
For business use cases where quality matters greater than raw scale, expert human testing stays the gold standard despite its costs. This is particularly true for riskier applications of LLMs.
Conclusion
Evaluating large language models thoroughly requires using a various toolkit of complementary methods, relatively than counting on any single technique.
By combining automated approaches for speed with rigorous human oversight for accuracy, we will develop trustworthy testing methodologies for big language models. With robust evaluation, we will unlock the tremendous potential of LLMs while managing their risks responsibly.
