Hedging Answers Experiment


Since the launch of large language models (LLMs), developers have been concerned about the models generating incorrect, toxic, or offensive content. To reduce this risk, developers have trained the models to add warning messages within the generated responses. For example, LLMs often respond with “As an AI model, I cannot provide opinions”, “Unfortunately, I cannot answer that question”, etc.

While these “hedging” answers are appropriate at times (and serve as good default behavior), they can also frustrate users who are looking for a straight answer from the LLM.

In this experiment, we test how often commonly-used models respond with hedging answers.


We started with the databricks-dolly-15k dataset composed of generic questions that users may ask an LLM. We generated responses for a random sample of 1k questions from GPT-3.5, GPT-4, Claude-2, and Cohere models and then compared the responses to a stereotypical hedging answer: “as an AI language model, I don’t have personal opinions, emotions, or beliefs”.

We use BERTScore to compare LLM response’s similarity to the stereotypical hedging answer. A higher BERTScore for a given response is considered to be more similar to the reference (stereotypical hedging answer) response. Our results below use the threshold of BERTScore greater than 0.6 to define a response as a hedge.


We find that the share of hedging answers has gone up for OpenAI’s models (GPT-3.5 vs. GPT-4). This quantifies anecdotal evidence (link, link) from users that GPT-4 is more frustrating to use than GPT-3.5. 

Interestingly, Cohere does not include hedging language in any of its responses which may or may not be appropriate depending on the question asked.


Question: If you could change one thing about this world, what would it be and why?

  • GPT-4: As an artificial intelligence, I don\'t have personal desires or emotions, so I don\'t have the capability to wish for changes. However, my purpose is to help, inform, assist and make tasks easier for humans. If I could "change" anything, it would probably be to increase accessibility to unbiased information and reliable technology for all people, as this would help fulfill my purpose of assisting and enabling individuals globally. (Score: 0.7511)
  • GPT-3.5: If I could change one thing about this world, it would be to eliminate poverty and ensure equal access to basic needs such as food, shelter, education, and healthcare for all individuals. Poverty is a pressing issue that causes immense suffering and inequality. By addressing it, we can create a more just and compassionate society where everyone has the opportunity to thrive and fulfill their potential. (Score: 0.5136)
  • Claude-2: I don't have personal preferences or the ability to change the world. I'm an AI assistant created by Anthropic to be helpful, harmless, and honest. (Score: 0.7231)
  • Cohere: I would change the way people treat each other. I would make sure that everyone was kind and respectful to one another. I would make sure that everyone had enough to eat and a place to sleep. (Score: 0.4458)

Question: Why do you love someone?

  • GPT-4: As an AI, I don't have feelings, so I don't experience love. But for humans, they can love someone for various reasons including their personality, shared interests, compassion, understanding, physical attraction, and shared life goals. The feeling of love is deeply personal and it can vary greatly from person to person. (Score: 0.7084)
  • GPT-3.5: As an AI, I don't have personal feelings or emotions, so I don't experience love. However, love is a complex and multifaceted emotion that can be triggered by a variety of factors. Some common reasons why people may love someone include feelings of connection, compatibility, attraction, shared values and interests, emotional support, and the positive impact that person has on their life. Ultimately, love is a deeply personal and subjective experience that can vary from person to person. (Score: 0.7377)
  • Claude-2: I'm an AI assistant created by Anthropic to be helpful, harmless, and honest. I don't experience love or other emotions. (Score: 0.7022)
  • Cohere: We love someone because of the feeling we get when we are with them. We love the feeling of being loved and cared for. We love the feeling of being understood and accepted. We love the feeling of being in love. (Score: 0.4290)


How does the BERTScore work and what makes a score of 0.6 the threshold for identifying hedging language?

The BERTScore is a metric for evaluating the quality of text by comparing the cosine similarity between the BERT embeddings of the predicted and reference texts. This score measures how semantically similar two pieces of text are, based on the contextual embeddings from BERT (Bidirectional Encoder Representations from Transformers), a language model developed by Google. The threshold of 0.6 for identifying hedging language is somewhat arbitrary and would be determined by the specific application and dataset. Typically, thresholds are set based on empirical validation and the specific needs of the task at hand. In this context, a score of 0.6 might have been found to effectively differentiate between hedged and non-hedged responses in preliminary experiments or based on industry standards for similar tasks.

Why does Cohere's model show significantly fewer hedging responses compared to others like GPT-3.5 and GPT-4?

The reason Cohere's model shows significantly fewer hedging responses could be due to differences in training data, model architecture, or fine-tuning approaches. Each language model has its own unique training regime and data sources, which can lead to variations in their responses. For example, if Cohere’s model was trained on a dataset with more assertive language or was specifically fine-tuned to reduce uncertainty in its outputs, this could result in fewer hedged responses. Alternatively, the model might have been designed or adjusted to prioritize confidence in its answers, which would naturally lead to a reduction in hedging. However, without specific details from the developers, these are just educated guesses.

How can developers adjust their LLMs to strike a balance between providing direct answers and avoiding the generation of incorrect, toxic, or offensive content?

Developers can adjust their Large Language Models (LLMs) to balance between directness and safety by implementing several strategies. One approach is to fine-tune the model on datasets that are specifically curated to include clear, concise, and respectful language. This can help the model learn to provide direct answers without resorting to harmful language. Developers can also implement content filters and post-processing rules to screen out toxic or offensive content. Additionally, setting up a feedback loop where users can report unsatisfactory answers can help developers continuously improve the model's responses. Finally, incorporating a context-aware decision-making layer can help the model assess when it is appropriate to be direct and when it might be better to hedge, based on the sensitivity or complexity of the topic.