Why You Should Encourage Your AI/LLMs to Say ‘I Don’t Know’

In AI and machine learning, providing accurate and timely information is crucial. However, equally important is an AI model’s ability to recognize when it doesn’t have enough information to answer a query and to gracefully decline to respond. This capability is a critical factor in maintaining the reliability and trustworthiness of the entire system.

Why AI needs to Say ‘I Don’t Know’

Imagine an AI model trained to answer questions on various topics. If asked about a recent event after its last update, it should recognize its lack of relevant information and decline to provide an answer. Failing to do so could lead to the dissemination of outdated or inaccurate information, eroding user trust and damaging the AI system’s credibility.

Encouraging your AI to say “I don’t know” when it genuinely doesn’t have the necessary information contributes to:

• Reduce Risks and Potential Harm: Avoids the risk of providing incorrect or misleading information.
• Building Trust: Users are more likely to trust a system that is transparent about its limitations.
• Improving User Experience: Ensures users receive reliable and accurate responses, enhancing overall satisfaction.

Factors to Consider Before Rejecting an Answer

Deciding when an AI should decline to answer can be complicated. Several factors should be considered to ensure the model makes the right decision:

• Data Freshness: Is the model’s knowledge up-to-date? If the training data is outdated, it may be wise to decline to answer queries related to recent events.
• Contextual Relevance: Does the query fall within the model’s domain of knowledge? If not, it’s better to avoid answering.
• Knowledge Gaps: Does the model have known gaps in its knowledge base? If a query touches on those gaps, the model should recognize this and decline to answer.
• Ambiguity in the Query: Is the question too vague or ambiguous? High ambiguity might indicate that the model is unsure and should refrain from answering.
• Complexity of the Question: Is the question too complex or layered? If the model struggles to parse or fully understand the question, it should consider not providing an answer.
• Confidence Score: What is the model’s confidence in its answer? If the confidence score is below a certain threshold, the model should consider not responding.

Tackling the ‘I Don’t Know’ Challenge

Here are some practical tips to help AI systems avoid answering questions, i.e., encourage them to say “I don’t know.

• Build Pre-Processing Layers: Design and deploy layers that process queries before they reach the model. These layers would assess ambiguity, relevance, and complexity, adjusting the queries or the model’s behavior accordingly. For instance, this module can flag ambiguous queries and either prompt the user for clarification or adjust the query to reduce ambiguity.
• Query Filtering: Implement a filter that analyzes the input query for its relevance to the domain the RAG or Model is designed for. If the relevance is low, the system can flag it, ask for a more relevant query, or adjust the model’s response generation.
• Post-Processing Analysis: After the model generates a response, run post-processing checks that apply the metrics you’ve designed (e.g., confidence, relevance, complexity) to evaluate whether the response meets your thresholds.
• Context-Based Query Rejection: Context-Based Query Rejection involves determining whether the context retrieved for a query has sufficient relevance. If the retrieved context does not align closely with the query based on cosine similarity or other measures, the model should decline to provide an answer due to “insufficient data.”
• Cosine Similarity Filtering: Implement a cosine similarity check between the query and the retrieved context. If the similarity score falls below a predefined threshold, the system should flag the query as having insufficient context and refrain from answering.
• Context Re-Ranking: Use re-ranking techniques to prioritize the most relevant contexts. If even the highest-ranked context is not sufficiently relevant, the query should be rejected.
• Time-Sensitivity Threshold: For time-sensitive queries, set a threshold based on the age of the data or data source. The model could refrain from answering if the data is older than a certain period.
• Threshold Setting: Establish a threshold for cosine similarity that the retrieved context must meet for the model to consider it relevant enough to generate a response. Adjust this threshold based on the domain’s sensitivity or the required accuracy level.

Encouraging your AI to say “I don’t know” when it lacks the necessary information is not a sign of weakness but a strength. It demonstrates the system’s commitment to accuracy, transparency, and user trust.