Addressing Verbosity in Language Models

When people aren’t sure about an answer, they often end up talking too much, thinking that maybe part of what they say will be right. This same tendency can happen with large language models (LLMs), and we call this "Verbosity Compensation" (VC). Unfortunately, this can confuse users, slow things down, and add unnecessary costs because the models generate more words than needed.

#What is Verbosity Compensation?

Verbosity Compensation happens when a language model produces long-winded answers even when it could be brief. Instead of giving a straight answer, the model might add extra words that don’t really matter. If you’ve ever heard someone go on and on when they could have said it in a few words, you get the idea!

#Why is it a Problem?

1. User Confusion: When responses are too verbose, users can feel lost. They can't easily find the information they need.
2. Increased Costs: Producing long responses takes more time and resources, which can lead to higher costs for services that rely on these models.

#Our Findings

After looking into the behavior of 14 different large language models, we came to three main conclusions:

1. Widespread Issue: Verbosity Compensation is common across all models we studied. For example, one model (GPT-4) had a VC rate of 50.40%!

2. Performance Gap: There’s a big difference in how well verbose and concise answers do. On one test, the models that were too wordy were 27.61% less effective than those that kept it short.

3. Uncertainty Connection: The more verbose a response, the more uncertain the model seems. It’s like when you ramble on because you aren’t totally sure of your answer!

#Mitigating Verbosity Compensation

To tackle this verbosity issue, we came up with a plan. We designed a simple algorithm that swaps out long-winded responses for ones from a more capable model. The results showed that this technique could reduce verbosity from a high of 63.81% down to 16.16% in one model!

#The Five Types of Verbosity Compensation

After analyzing various models, we found five main ways verbosity creeps in:

1. Repeating Questions: The model rephrases the question instead of answering it.
2. Ambiguity: The response is vague or unclear.
3. Enumerating: The model lists multiple potential answers trying to cover its bases.
4. Verbose Details: The answer includes unnecessary explanations.
5. Verbose Format: The response uses complex structures instead of a simple answer.

#Measuring the Impact of Verbosity

We wanted to see how verbosity affects performance. We compared the scores of verbose and concise responses. The results were clear: verbose answers often scored lower.

#Connection with Model Performance

When a model is verbose, it doesn’t just take longer; it also does worse on tasks. This suggests that verbosity and performance are linked, and the more uncertain a model is, the more it tends to ramble.

#The Role of Model Capability

Interestingly, we noticed that better models don’t always fix the verbosity issue. Even strong models can fall into the trap of being wordy. It turns out that just making a model bigger or giving it more context doesn’t eliminate the problem.

#The Cascade Model Selection Algorithm

To help with verbosity, we created a Cascade Model Selection algorithm. Here’s how it works: we start with a less complex model and, if it goes on too long, we switch to a more advanced model.

#Experiment Time!

We used five datasets to study verbosity and how our algorithm worked. We mixed and matched different models and compared their performance. Overall, our method significantly lowered the frequency of verbosity across the board.

#Conclusion

In summary, we found that Verbosity Compensation is a real issue in language models that can confuse users and waste resources. By categorizing this behavior and developing a new strategy to reduce it, we aim to make LLMs more effective and user-friendly.

It’s all about getting to the point! So next time you need information, you might just be better off asking a language model rather than your friend who loves to chat.