Around 2000 years ago, a priestess named Pythia used to predict people's future. However, the predictions have often been cryptic, leaving them open to various interpretations depending on people's perceptions. 

Large language models (LLMs) today imitate Pythia by generating ambiguous and unreliable outputs. For example, ChatGPT displayed a 31% hallucination rate when asked to generate scientific abstracts. These hallucinations lead to unwanted scenarios, including financial loss, erosion of trust, perpetuating bias, and safety concerns. 

Harmful consequences emphasize the need for a hallucination detection tool that identifies hallucinations in LLM outputs and alerts the authorities. 

To accomplish this, Wisecube presents Pythia, a state-of-the-art hallucination detection library. Pythia is specifically designed to mitigate the challenges of AI hallucinations in LLMs with its unique functionality. 

In this blog post, we uncover the revolutionizing benefits of Pythia and how it detects hallucinations in LLM responses.

Pythia: The Oracle of Delphi

Photo by Ann Ronan Pictures/Print Collector/Getty Images

Pythia was a priestess in ancient Greece. People worldwide would visit the temple of Apollo, Delphi, to consult the priestess on important matters. She delivered prophecies believed to have been sent to her by God. 

However, The prophecies of Pythia were often ambiguous, and people would interpret them based on their understanding. Usually unclear, the prophecies of Pythia were sometimes frenzied warnings of doom and destruction. Despite the cryptic messages, people relied on her to predict their fate and guide their actions. Historians believe the priestess’ hallucinations were due to the toxic fumes present in her temple’s geology, but the exact cause remains a history. 

Like the Greek priestess, AI models can generate ambiguous, inaccurate, and misleading outputs. However, we know the reasons behind AI hallucinations, which makes it possible to minimize them in AI-generated outputs. Some of the reasons include:

• Bias in training data

• Bias in algorithms

• Malicious attacks

• Lack of context understanding

Amidst these underlying factors, AI hallucinations can disrupt businesses and threaten human lives. This emphasizes the need for a modern Pythia that yields accurate and reliable outputs to speed up the research process and revolutionize healthcare research.

The Need to Detect Hallucinations in LLMs

The global generative AI market is projected to grow at a compound annual growth rate of 31.5% by 2030. This indicates increasing adoption of LLMs across industries such as healthcare, finance, and law. However, industry-leading LLMs have a 27% hallucination rate, and general LLMs have a 15-20% hallucination rate, indicating the likelihood of misleading responses. 

Despite the increasing use of LLMs for research purposes, the challenges of LLM hallucinations can have far-reaching consequences. In critical domains where LLM accuracy is critical, hallucinations pose a financial, reputational, and legal threat to industries. Misleading outputs, hate speech, and unreliable diagnoses need to be subsided to leverage the full potential of LLMs. Addressing these challenges will open the doors to reliable and trustworthy AI systems, improving confidence and revolutionizing research.  

Wisecube’s Pythia: AI Hallucination Detection Tool

Understanding the need for trustworthy AI systems, Wisecube offers Pythia as a state-of-the-art hallucination detection tool in LLMs. It surpasses the ancient Greek priestess Pythia by allowing healthcare researchers to generate factually correct outputs.

Powered by Wisecube Claim Extractor, Pythia detects and flags hallucinations in real-time. When an LLM generates an output, Pythia extracts knowledge triplets from LLM responses and its knowledge base to detect deviation among them. Each triplet represents <subject, predicate, object>, capturing precise details and relationships among the components.  

After comparing the LLM response and its references, Pythia categorizes hallucinations into four categories:

1. Entailment

These claims are present in both LLM responses and their references, indicating accurate outputs.

2. Contradiction

These claims are present in LLM responses but disregarded by references, representing hallucinations in LLM outputs.

3. Missing facts

Missing facts are present in the references but disregarded by LLM responses, representing gaps in LLM responses.

4. Neutral

Neutral claims are present in LLM responses but are neither contradicted nor confirmed by the references, representing an opportunity further to verify the responses against a broader knowledge base.

Based on this categorization, Pythia generates a report guiding developers toward LLM improvement. The report highlights the system's strengths and weaknesses, allowing developers to identify patterns and diagnose issues in their systems.

When integrated into an LLM, hallucination detection in Pythia takes place in the following steps:

1. A user presents a query to an LLM, which generates a response based on its learning, knowledge base, and context understanding.

2. Pythia monitors the LLM responses in real-time and captures knowledge triplets from it and references.

3. Pythia compares response knowledge triplets against the references and flags them as Entailment, Contradiction, Missing facts, and Neutral claims.

4. Pythia generates an aggregate report based on the hallucinations in LLM responses. This report guides developers in improving their systems.

Benefits of Integrating Pythia for Hallucination Detection

Drawing inspiration from knowledge graphs, Wisecube’s Pythia promises many benefits, transforming the use of LLMs for healthcare research. The benefits that set Pythia apart from other hallucination detection techniques include:

Advanced Hallucination Detection

The use of knowledge triplets for response reference comparisons allows Pythia to capture finer details and the underlying context in the content. Continuous analysis and aggregate reports lead to continuous improvement of LLMs, encouraging accurate outputs.

Seamless Integration With Langchain

Pythia smoothly integrates within the Langchain ecosystem so that developers can leverage its full potential with effortless interoperability. This integration opens doors to boundless possibilities in biomedical research by empowering developers to develop trustworthy AI systems.

Customizable Detection

Pythia is flexible to meet specific system requirements. Developers can configure it to suit their use cases, enhancing flexibility and increased accuracy. Customizable detection allows the development of tailored solutions to meet the needs of various industries.

Real-time Analysis

When integrated into an AI system, Pythia monitors its responses in real time, detecting and flagging hallucinations as they occur. Real-time analysis allows prompt action to address underlying issues, ensuring the reliability of AI-generated content. 

Enhanced Trust in AI

Real-time hallucination detection and quick diagnosis of issues improve LLM performance, resulting in accurate and reliable outputs. This encourages users to trust AI systems and adopt them for innovative research.

Advanced Privacy

Pythia protects your information so you can use its functionalities without compromising your proprietary data. Advanced privacy protection allows developers to build robust AI systems without worrying about losing or compromising their data.

With its promising features, Wisecube offers boundless possibilities for revolutions in healthcare. LLMs that understand the context and yield verified outputs have become necessary for AI's better future, and Wisecube addresses this need with its cutting-edge products.

Contact us today to get started with Pythia, diminish the challenges of AI hallucinations, and develop cutting-edge LLMs.