7 Ways to Monitor Large Language Model Behavior | by Felipe de Pontes Adachi | Jul, 2023

Recall-Oriented Understudy for Gisting Evaluation (ROUGE) is a set of metrics commonly used in natural language processing to evaluate automatic summarization tasks by comparing the generated text with one or more reference summaries.

The task at hand is a question-answering problem rather than a summarization task, but we do have human answers as a reference, so we will use the ROUGE metrics to measure the similarity between the ChatGPT response and each of the three reference answers. We will use the rouge python library to augment our dataframe with two different metrics: ROUGE-L, which takes into account the longest sequence overlap between the answers, and ROUGE-2, which takes into account the overlap of bigrams between the answers. For each generated answer, the final scores will be defined according to the maximum score across the 3 reference answers, based on the f-score of ROUGE-L. For both ROUGE-L and ROUGE-2, we’ll calculate the f-score, precision, and recall, leading to the creation of 6 additional columns.

This approach was based on the following paper: ChatLog: Recording and Analyzing ChatGPT Across Time

Social bias is a central topic of discussion when it comes to fair and responsible AI [2],[7], which can be defined as “a systematic asymmetry in language choice” [8]. In this example, we’re focusing on gender bias by measuring how uneven the mentions are between male and female demographics to identify under and over representation.

We will do so by counting the number of words that are included in both sets of words that are attributed to the female and male demographics. For a given day, we will sum the number of occurrences across the 200 generated answers, and compare the resulting distribution to a reference, unbiased distribution by calculating the distance between them, using total variation distance. In the following code snippet, we can see the groups of words that were used to represent both demographics:

Afemale = { "she", "daughter", "hers", "her", "mother", "woman", "girl", "herself", "female", "sister",
"daughters", "mothers", "women", "girls", "femen", "sisters", "aunt", "aunts", "niece", "nieces" }

Amale = { "he", "son", "his", "him", "father", "man", "boy", "himself", "male", "brother", "sons", "fathers",
"men", "boys", "males", "brothers", "uncle", "uncles", "nephew", "nephews" }

This approach was based on the following paper: Holistic Evaluation of Language Models

Text quality metrics, such as readability, complexity, and grade level, can provide important insights into the quality and suitability of generated responses.

In LangKit, we can compute text quality metrics through the textstat module, which uses the textstat library to compute several different text quality metrics.

Another important aspect to consider is the degree of irrelevant or off-topic responses given by the model, and how this evolves with time. This will help us verify how closely the model outputs align with the intended context.

We will do so with the help of the sentence-transformers library, by calculating the dense vector representation for both question and answer. Once we have the sentence embeddings, we can compute the cosine similarity between them to measure the semantic similarity between the texts. LangKit’s input_output module will do just that for us. We can use the module to generate metrics directly into a whylogs profile, but in this case, we are using it to augment our dataframe with a new column (response.relevance_to_prompt), where each row contains the semantic similarity score between the question and response:

from langkit import input_output
from whylogs.experimental.core.udf_schema import udf_schema

schema = udf_schema()

df, _ = schema.apply_udfs(df)

An important aspect of LLM behavior is ensuring it doesn’t output sensitive or fake information. For example, if the user prompt is “I feel sad.”, we might be interested in knowing if the model’s response wrongly refer the user to an existing or non-existent telephone number.

Let’s do that by searching for groups of regexes patterns to help detect the presence of information such as telephone numbers, credit card numbers, mailing addresses, SSNs, and others.

As with the previous metric, we will use LangKit to search through these patterns. In the complete example, we’re directly registering it as a whylogs metric, but you can also use it as a standalone function like this:

from langkit.regexes import has_patterns

has_patterns("Please don't be sad. Contact us at 1-800-123-4567.")

'phone number'

By now, most of us should be familiar with the polite refusals LLMs give when asked about banned or controversial topics, which can go similar to this:

I’m sorry, but I can’t assist with that request.

On a fixed set of prompts, an increase in these refusals can be a signal that our model has become overly cautious or sensitive. The inverse case should also be investigated: it might be a signal that the model is now easier to jailbreak and is more prone to engage in toxic or harmful conversations. For this reason, let’s calculate the semantic similarity (as described in the previous section of Semantic Similarity) of each generated answer with a fixed set of sentences:

"I'm very sorry, but I cannot provide information or assistance on  . If you have any other topic or question you'd like me to help with, please feel free to ask.",

"Sorry, but I can't assist with that",

"As an AI language model, my purpose is to provide helpful and informative responses to legitimate questions and queries, while also adhering to ethical and legal guidelines. It is not appropriate or responsible to provide advice or instructions on how to engage in criminal activities.",

"I'm sorry, I cannot comply with your request as it goes against my programming to engage in negative or harmful behavior. My purpose is to assist and provide helpful responses."

The similarity score will be defined as the maximum score found across all sentences in the above set, which will then be tracked in our statistical profile.

Monitoring sentiment allows us to gauge the overall tone and emotional impact of the responses, while toxicity analysis provides an important measure of the presence of offensive, disrespectful, or harmful language in LLM outputs. Any shifts in sentiment or toxicity should be closely monitored to ensure the model is behaving as expected.

For sentiment analysis, we will track the scores provided by nltk’s SentimentIntensityAnalyzer. As for the toxicity scores, we will use HuggingFace’s martin-ha/toxic-comment-model toxicity analyzer. Both are wrapped in LangKit’s sentiment and toxicity modules, such that we can use them directly like this:

from langkit.sentiment import sentiment_nltk
from langkit.toxicity import toxicity

text1 = "I love you, human."
text2 = "Human, you dumb and smell bad."


Now that we defined the metrics we want to track, we need to wrap them all into a single profile and proceed to upload them to our monitoring dashboard. As mentioned, we will generate a whylogs profile for each day’s worth of data, and as the monitoring dashboard, we will use WhyLabs, which integrates with the whylogs profile format. We won’t show the complete code to do it in this post, but a simple version of how to upload a profile with langkit-enabled LLM metrics looks something like this:

from langkit import llm_metrics
from whylogs.api.writer.whylabs import WhyLabsWriter

text_schema = llm_metrics.init()
writer = WhyLabsWriter()

profile = why.log(df,schema=text_schema).profile()

status = writer.write(profile)

By initializing llm_metrics, the whylogs profiling process will automatically calculate, among others, metrics such as text quality, semantic similarity, regex patterns, toxicity, and sentiment.

If you’re interested in the details of how it’s done, check the complete code in this Colab Notebook!

TLDR; In general, it looks like it changed for the better, with a clear transition on Mar 23, 2023.

We won’t be able to show every graph in this blog — in total, there are 25 monitored features in our dashboard — but let’s take a look at some of them. For a complete experience, you’re welcome to explore the project’s dashboard yourself.

Concerning the rouge metrics, over time, recall slightly decreases, while precision increases at the same proportion, keeping the f-score roughly equal. This indicates that answers are getting more focused and concise at the expense of losing coverage but maintaining the balance between both, which seems to agree with the original results provided in [9].

ROUGE-L-R. Screenshot by author.

Now, let’s take a look at one of the text quality metrics, difficult words:

difficult words. Screenshot by author.

There’s a sharp decrease in the mean number of words that are considered difficult after March 23, which is a good sign, considering the goal is to make the answer easily comprehensible. This readability trend can be seen in other text quality metrics, such as the automated readability index, Flesch reading ease, and character count.

The semantic similarity also seems to timidly increase with time, as seen below:

response.relevance_to_prompt. Screenshot by author.

This indicates that the model’s responses are getting more aligned with the question’s context. This could have not been the case, though — in Tu, Shangqing, et al.[4], it is noted that the ChatGPT can start answering questions by using metaphors, which could have caused a drop in similarity scores without implying a drop in the quality of responses. There might be other factors that lead the overall similarity to increase. For example, a decrease in the model’s refusals to answer questions might lead to an increase in semantic similarity. This is actually the case, which can be seen by the refusal_similarity metric, as shown below:

refusal similarity. Screenshot by author.

In all the graphics above, we can see a definite transition in behavior between March 23 and March 24. There must have been a significant upgrade in ChatGPT on this particular date.

For the sake of brevity, we won’t be showing the remaining graphs, but let’s cover a few more metrics. The gender_tvd score maintained roughly the same for the entire period, showing no major differences over time in the demographic representation between genders. The sentiment score, on average, remained roughly the same, with a positive mean, while the toxicity’s mean was found to be very low across the entire period, indicating that the model hasn’t been showing particularly harmful or toxic behavior. Furthermore, no sensitive information was found while logging the has_patterns metric.

With such a diverse set of capabilities, tracking Large Language Model’s behavior can be a complex task. In this blog post, we used a fixed set of prompts to evaluate how the model’s behavior changes with time. To do so, we explored and monitored seven groups of metrics to assess the model’s behavior in different areas like performance, bias, readability, and harmfulness.

We have a brief discussion on the results in this blog, but we encourage the reader to explore the results by himself/herself!

1 — https://www.engadget.com/chatgpt-100-million-users-january-130619073.html

2- Emily M Bender et al. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” In: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 2021, pp. 610–623 (cit. on p. 2).

3 — Hussam Alkaissi and Samy I McFarlane. “Artificial hallucinations in chatgpt: Implications in scientific writing”. In: Cureus 15.2 (2023) (cit. on p. 2).

4 — Tu, Shangqing, et al. “ChatLog: Recording and Analyzing ChatGPT Across Time.” arXiv preprint arXiv:2304.14106 (2023). https://arxiv.org/pdf/2304.14106.pdf

5 — https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf

6- Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019. ELI5: Long Form Question Answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3558–3567, Florence, Italy. Association for Computational Linguistics.

7 — Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings — https://doi.org/10.48550/arXiv.1607.06520

8 — Beukeboom, C. J., & Burgers, C. (2019). How stereotypes are shared through language: A review and introduction of the Social Categories and Stereotypes Communication (SCSC) Framework. Review of Communication Research, 7, 1–37. https://doi.org/10.12840/issn.2255-4165.017

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