The text outlines a method for evaluating the reliability of AI-generated text, particularly chatbot responses, to detect potential inaccuracies or fabrications. By comparing the consistency of multiple responses generated by a language model and evaluating their similarity using various methods like cosine similarity, BERTScore, and natural language inference, the goal is to reduce the likelihood of misleading or erroneous information. The approach also involves using a large language model to evaluate the outputs of other similar models. The ultimate objective of this novel approach is to enable AI systems to self-identify and rectify inconsistencies, thereby potentially improving their trustworthiness.
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Teaching Chatbots to Say “I Don’t Know”
Introduction
Teaching chatbots to acknowledge their limitations is crucial to ensure accurate and reliable responses. In this article, we explore practical solutions to detect and prevent chatbot hallucinations, where they generate fictional information.
Sample-Based Hallucination Detection
We introduce a sample-based hallucination detection mechanism that compares the outputs of the language model. By evaluating the semantic consistency of multiple responses to the same prompt, we can identify potential hallucinations.
Sentence Embeddings Cosine Distance
We utilize sentence embeddings and compute pairwise cosine similarity to measure the semantic similarity between the original response and the sample outputs. This provides a quick and effective method for assessing output consistency.
SelfCheckGPT-BERTScore
We implement the BERTScore, which utilizes contextual embeddings to evaluate the similarity between the original response and the sample outputs at the sentence level. This method provides a more detailed assessment of output accuracy.
SelfCheckGPT-NLI
Utilizing natural language inference (NLI), we determine the logical relationship between the original response and the sample outputs, classifying them as entailment, contradiction, or neutral. This approach offers a comprehensive evaluation of output consistency.
SelfCheckGPT-Prompt
We leverage the language model itself to evaluate the generated text by sending the output and sample responses to an AI model for consistency assessment. This method provides real-time evaluation with minimal computational overhead.
Real-Time Hallucination Detection
We demonstrate the development of a Streamlit app for real-time hallucination detection, utilizing the LLM self-similarity score to determine whether to display the generated output or a disclaimer.
Conclusion
The techniques presented offer promising approaches to detect and prevent chatbot hallucinations, paving the way for more reliable and trustworthy AI interactions. By leveraging AI for quality assurance, companies can enhance customer engagement and operational efficiency.
References
- BERTSCORE: EVALUATING TEXT GENERATION WITH BERT
- SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
- A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
AI Solutions for Your Business
If you want to evolve your company with AI, stay competitive, and use AI to your advantage, consider How to Detect Hallucinations in LLMs. Discover how AI can redefine your way of work and identify automation opportunities, define KPIs, select an AI solution, and implement gradually. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com. Explore practical AI solutions such as the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement and manage interactions across all customer journey stages.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- How to Detect Hallucinations in LLMs
- Towards Data Science – Medium
- Twitter – @itinaicom
