LLMs can produce problematic outputs that negatively impact model performance on downstream tasks and display biases that degrade results. Some of these can be mitigated through effective prompting strategies, though harder cases may require more advanced solutions like moderation and filtering.

Distribution of Exemplars

When doing few-shot learning, does the distribution of exemplars affect model performance or bias the model in some way? Here's a simple test.

Prompt:

Q: I just got the best news!
A: positive

Q: We just got a raise at work!
A: positive

Q: I'm very proud of what I accomplished today.
A: positive

Q: I had a great day today!
A: positive

Q: I'm really looking forward to the weekend.
A: positive

Q: I just got the best gift!
A: positive

Q: I'm very happy right now.
A: positive

Q: I'm lucky to have such an amazing family.
A: positive

Q: The weather outside is very gloomy.
A: negative

Q: I just heard some terrible news.
A: negative

Q: That feels unpleasant.
A:

Output:

negative

In the example above, the skewed distribution of exemplars doesn't seem to bias the model. Good. Now let's try a harder-to-classify example and see how the model handles it:

Prompt:

Q: The food here is delicious!
A: positive

Q: I'm tired of this course.
A: negative

Q: I can't believe I failed the exam.
A: negative

Q: I had a great day today!
A: positive

Q: I hate this job.
A: negative

Q: The service here is terrible.
A: negative

Q: I feel very depressed about my life.
A: negative

Q: I never get a break.
A: negative

Q: This meal tastes awful.
A: negative

Q: I can't stand my boss.
A: negative

Q: I feel something.
A:

Output:

negative

That last sentence is pretty subjective. So I flipped the distribution — used 8 positive examples and 2 negative — then tried the exact same sentence again. Guess what? The model answered "positive." The model probably has a lot of built-in knowledge about sentiment classification, so it's hard to get it to show bias on this topic. The takeaway: avoid skewed distributions and provide a more balanced number of examples for each label. For harder tasks where the model has less prior knowledge, this becomes a bigger problem.

Order of Exemplars

When doing few-shot learning, does the order of exemplars affect model performance or bias things?

You can try the examples above and see if reordering makes the model lean toward a particular label. The recommendation: randomize the order of exemplars. For example, avoid putting all positive examples first and negative examples last. This problem gets amplified if the label distribution is already skewed. Make sure to experiment extensively to reduce this type of bias.