In this article, you will learn a clear and practical framework for diagnosing why language models perform poorly and how to quickly examine possible causes.
Topics covered include:
- 5 common failure modes and what they look like
- Concrete diagnostics that can be performed immediately
- Practical mitigation tips for each disorder
Let’s not waste any more time.
How to diagnose why a language model fails
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introduction
language modelAlthough very useful, it is not perfect and can fail or exhibit undesirable performance due to various factors such as data quality, tokenization constraints, and difficulty interpreting user prompts correctly.
In this article, we take a diagnostic perspective and explore a five-point framework for understanding why language models, whether large, general-purpose large-scale language models (LLMs) or small, domain-specific language models, do not behave well.
Language model diagnostic points
In the following sections, we identify common reasons for language model failure, briefly explain each, and provide practical tips for diagnosis and solutions.
1. Training data is of poor or insufficient quality
Like other machine learning models such as classifiers and regressors, the performance of language models is highly dependent on the amount and quality of data used for training, but there is one not-so-subtle nuance. That is, language models are often trained on very large datasets or text corpora, ranging from thousands to millions or even billions of documents.
If your language model produces inconsistent, factually incorrect, or nonsensical (hallucinatory) output even for simple prompts, the training data used may not be of sufficient quality or quantity. Specifically, the training corpus may be too small, old, noisy, biased, or full of irrelevant text. For smaller language models, this data-related issue can also include missing domain vocabulary in the generated answers.
To diagnose data problems, examine a sufficiently representative portion of the training data, if possible, and analyze properties such as relevance, coverage, and topic balance. Targeted prompting for known facts and using unusual terminology to identify gaps in knowledge are also effective diagnostic strategies. Finally, keep a reliable reference dataset on hand to compare the generated output with the information it contains.
If your language model produces inconsistent, factually incorrect, or nonsensical (hallucinatory) output even for simple prompts, the training data used may not be of sufficient quality or quantity.
2. Tokenization or Vocabulary Limitations
Suppose that when you analyze the inner workings of a newly trained language model, it appears that the language model struggles with certain words or symbols in its vocabulary, splitting them into tokens in unexpected ways or failing to represent them properly. This may be due to the tokenizer used in conjunction with the model not working properly with the target domain, resulting in less-than-ideal handling of uncommon words or jargon.
Diagnosing tokenization and vocabulary issues involves inspecting the tokenizer, that is, looking at how domain-specific terms are partitioned. Metrics such as perplexity and log-likelihood of retained subsets can quantify how well a model represents the domain text, and testing for edge cases (such as words or symbols with non-Latin or unusual Unicode characters) can help identify root causes related to token management.
3. Easily unstable and sensitive
Small changes in prompt wording, punctuation, or the order of multiple non-sequential instructions can significantly change the quality, accuracy, and relevance of the output produced. It’s the instability and sensitivity of the prompt. The language model becomes overly sensitive to how the prompt is worded. This is often because they have not been properly fine-tuned to follow effective and detailed instructions, or because there are discrepancies in the training data.
The best way to diagnose prompt instability is experimentation. Try a series of paraphrased prompts that are equivalent in overall meaning and compare how consistent the results are with each other. Similarly, identify patterns in which prompts result in stable and unstable responses.
4. Context windows and memory constraints
If a language model fails to use the context introduced in previous interactions as part of a conversation with the user, or if it misses previous context within a long document, the language model can begin to exhibit undesirable patterns of behavior, such as repeating itself or contradicting what it previously “said.” The amount of context that a language model can hold, or context window, is primarily determined by memory limitations. Therefore, a context window that is too short may truncate relevant information and cause previous cues to be lost, whereas a context window that is too long may impede long-distance dependency tracking.
Diagnosing problems related to context windows and memory limits requires evaluating the language model repeatedly with increasingly longer inputs and carefully measuring how accurately it can call from previous parts. When attention visualization is available, it can be a powerful resource for seeing whether related tokens are receiving attention over long stretches of text.
5. Drifting in domain and time
Once deployed, language models are subject to the possibility of providing incorrect answers. For example, answers that are outdated, miss recently coined terms or concepts, or do not reflect evolving domain knowledge. This means that training data is fixed in the past and may still rely on snapshots of a world that has already changed. Therefore, changes in facts inevitably lead to decreased knowledge and decreased performance. This is similar to data and concept drift in other types of machine learning systems.
To diagnose temporal or domain-related drift, continually compile benchmarks for new events, terms, articles, and other relevant material in your target domain. Track the accuracy of answers using these new language items compared to answers related to stable or timeless knowledge to see if there are any significant differences. Additionally, schedule a regular performance monitoring scheme based on “new queries”.
final thoughts
In this article, we considered some common reasons why language models may not behave properly, from data quality issues to poorly managed context and production drift caused by changing factual knowledge. Language models are necessarily complex. Therefore, understanding the possible causes of failure and how to diagnose it is the key to making it more robust and effective.
