6 November 2024
Enhancing Chatbot Insights through Dimensionality Reduction
Enhancing Chatbot Insights through Dimensionality Reduction
Challenge
When using a chatbot, one of the primary issues is that it might provide answers that are not directly relevant to the user’s specific question or context. This can happen due to the chatbot’s limitations in accurately interpreting the user’s intent, especially if the query is complex or ambiguous. As a result, users may receive generic or off-topic responses that do not address their needs effectively. Additionally, chatbots may struggle to handle follow-up questions or nuanced discussions, leading to a disjointed conversation that can frustrate users. This misalignment between user expectations and the chatbot’s responses can diminish the overall effectiveness of the interaction.
Solution
Building on these challenges, BI4ALL created a Generative AI framework which includes a visualisation component that uses dimensionality reduction techniques to address some of these challenges. Specifically, question-and-answer vectors and vectors formed from document sections (chunks) are processed using a dimensionality reduction technique to reduce them to only three dimensions. This reduction enables these vectors to be represented as points in a 3D visualisation. To enhance usability, filters can be applied to the 3D visualisation, allowing the users to restrict it to points of particular interest, thus facilitating better perception and analysis.
Benefits
This technique offers several benefits: it helps developers analyse user feedback on responses to identify potential errors, detects chatbot hallucinations, and visually confirms that relevant documents are correctly indexed. Additionally, it helps users verify whether the response’s content matches the information in the corresponding chunk.
By visualising the data this way, the framework aims to provide clearer insights into the relationships between chunks, answers and questions, improving the chatbot’s ability to generate more relevant and contextually appropriate responses. Ultimately, the visualisation component contributes to a more natural and detailed study of the data, increasing the overall engagement experience and the accuracy of the chatbot’s responses.
Stats
50%
faster
in identifying related documents and questions
80%
faster
in confirming documents indexed
Practical Applications
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Support for information management
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Support for decision-making
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Find the most common topics researched/questioned
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Evaluate virtual assistants' performance
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Identify questions out of the scope of the company
Example
Consider a large enterprise deploying a virtual assistant to handle employee queries about HR policies and IT support. By implementing this dimensionality reduction technique, the company can visualise how well the documents and answers match different questions. For instance, they might discover that while the assistant does well at answering IT-related queries, it struggles with HR policy questions. The visualisation would reveal these deficiencies, allowing the team to refine the assistant’s training data and improve its overall performance. As a result, employees receive more accurate and helpful responses, enhancing their experience and trust in the virtual assistant.