Understand why hallucination detection is important for reliable LLMs. Learn the characteristics of a good hallucination detector.

From speech recognition to text generation, generative AI and LLMs have become involved in various applications and scenarios. The generative AI industry is projected to become a $1.3 Trillion market by 2032, with a compound annual growth rate (CAGR) of 42%. Generative AI products are predicted to add about $289 billion of new revenue to the software industry.

These projections for expansion reflect the presence of LLMs in all aspects of life, including healthcare. Their pervasive presence emphasizes the need for reliable AI to protect end-users from misleading decisions. However, LLMs often produce hallucinated outputs, resulting in penalties, lost trust in AI, reputation damage, and human life.

Let’s explore the need for AI reliability and how to achieve reliable AI solutions.

Why Does AI Reliability Matter?

Though LLMs have become increasingly intelligent, they still tend to hallucinate. AI hallucinations are AI-generated outputs that seem confident but are inaccurate and misleading. 

Industry-leading LLMs like ChatGPT have a 31% hallucination rate when used for scientific purposes. These hallucinations have significant costs, including:

• Healthcare costs: Misdiagnoses can lead to deaths and serious disabilities. It can also cost healthcare providers significant losses in penalties and reputation damage.

• Litigation costs: AI hallucinations can cause legal penalties in cases such as privacy violations or sensitive remarks. Legal errors can average $250,000 in settlements.

• Financial sector: When LLMs are used for research purposes, hallucinations can guide researchers toward poor decision-making. Poor investment decisions can lead to millions in losses.

• Operational downtime: Erroneous or misleading information can lead to disruptions or failures in operational processes. AI-related downtime can cost $10,000 per hour.

• Brand damage: Misleading outputs can damage a company's reputation and result in client hostility. 

Governments and NGOs are becoming involved in achieving reliable AI. For example, the EU AI Act is a European regulation on AI protection that covers various aspects of AI development and deployment to ensure safe AI. 

Complying with these regulations requires the development of reliableLLMs that produce reliable outputs. 

Existing AI Reliability Solutions and Their Challenges

Existing AI reliability solutions include bias detection, explainable AI (XAI) methods, adversarial techniques, etc. These solutions detect hallucinations and ensure reliable AI to some extent, but their limitations keep them from achieving safe and accurate AI.

They lack continuous monitoring support, transparency, and claim verification, which gives rise to their limitations, including:

1. Limited scalability: Current methods depend on the availability of high-quality training data, which prevents them from offering scalable AI reliability solutions.

2. Lack of explainability: Current methods fail to explain why a particular LLM response is flagged as a hallucination, making it difficult for developers to fix factual issues.

3. Low accuracy: Existing solutions are not as accurate, making them ineffective for mission-critical applications like life sciences.

10 Things to Look for in an AI Reliability Solution

AI reliability solutions must meet certain criteria to effectively detect hallucinations and offer transparency in their decisions. Here are the ten key things to look for in an AI reliability solution:

1. LLM Usage Scenarios

An AI reliability solution should accommodate different LLM usage scenarios. The three LLM usage scenarios include:

1. Zero context: The AI reliability solution should be able to directly compare references it finds with the LLM’s responses to assess accuracy.

2. Noisy context: When a question includes some context but is either noisy or incomplete, the AI reliability solution must be capable of consulting authoritative data before the LLM generates a response.

3. Accurate context: When the context is complete and reliable, it can use the references to provide a comprehensive summary or information in response.

LLM usage scenarios

These scenarios/ contexts guide the reliability solution in finding references and verifying LLM claims. 

2. Claim Extraction

The hallucination detection solutions should be able to decompose LLM responses into knowledge triplets. Unlike traditional methods, knowledge triplets extract granular details from content and highlight the relationship among words within a sentence. 

Knowledge triplets represent <subject, predicate, object>, highlighting the connection between the three and creating a better contextual understanding. For example:

Knowledge Triplet Example

3. Claim Categorization

After the claim extraction from LLM responses and references/ knowledge base, the hallucination detector must be able to categorize them based on their hallucination levels. Claim categorization guides developers toward the improvement of LLM by offering insights into its capabilities. The four categories for claim categorization include:

1. Entailment: Claims in both response and references, indicating accurate outputs.

2. Contradiction: Claims present in LLM responses but disregarded by references.

3. Missing facts: Claims present in references but absent in LLM responses, representing gaps in LLM responses.

4. Neutral: Claims present in LLM response but are neither contradicted nor confirmed by the references. 

Claim categorization based on hallucination level

4. Accuracy Metrics

An AI monitoring tool must be able to calculate the overall accuracy of LLM based on claim categorization. The accuracy metric represents the proportion of factually correct claims in the LLM response. Mathematically, accuracy is measured as:

Where:

• Entailment: Claims flagged as Entailment

• Contradiction: Claims flagged as Contradiction

• Reliability: Calculated using external data (i.e., knowledge graphs or RAGs)

5. Task Specific Metrics

There are two types of task-specific metrics, i.e., Core metrics specialized to the task and additional metrics for measuring the quality of the responses with respect to the task.

Specializing core metrics rely on the type of AI checker being used:

1. LLM-based checkers can enhance their accuracy by considering additional context provided in the form of a question when generating claim classification.

2. NLI-based checkers utilize a QA (Question Answering) evaluator model to verify the alignment of a response with a question. 

3. Knowledge graph-based checkers rely on Knowledge Graphs (KGs) to verify structured information about entities, relationships, and concepts. 

6. Systematic Error

The AI hallucination detector must be able to identify systematic errors. Systematic errors are recurring inconsistencies in LLM responses. Identifying them involves:

1. Comparing the reference information to itself. This means using the same reference as the knowledge base and the standard for comparison.

2. Generating core metrics to assess LLM performance. Metrics highlight the system's ability to classify claims as true, false, or neutral correctly.

3. Defining a systematic error checker, which is the harmonic mean of the complement of Entailment and Contradiction.

The ideal result is 100% Entailment, 0% Contradiction, and 0% Neutral claims. Mathematically, a systematic error checker is represented as:

7. Robust Set of Validators

An AI reliability solution should use a robust set of input/ output validators to protect sensitive information from leakage, misleading outputs, and toxic outputs. These validators validate LLM inputs and responses to ensure they meet quality standards and are accurate. The validators also protect against cyber attacks such as malicious input or prompt injections.

Example Input/Output Validators

8. Reporting

A good hallucination detection solution provides reports highlighting hallucination trends over time. These reports give insight into model improvement with time and systematic errors. Updated reports allow targeted adjustments to enhance the LLM’s reliability and effectiveness. 

Moreover, detailed analysis of these trends helps understand the underlying causes of hallucinations, resulting in better training and fine-tuning.

9. Continuous Alerting and Monitoring

Continuous monitoring and alerting are crucial for real-time hallucination detection and on-time correction. Continuous monitoring keeps track of LLM outputs against each user query, highlighting a model’s strengths and weaknesses. Alerting systems send email/SMS notifications about LLM performance, which helps address bias in model outputs before it perpetuates bias in the public. 

Real-time Monitoring and Alert Rules Example

10. LangChain Integration

LangChain is a Natural Language Processing (NLP) framework that provides pre-trained models and tools to customize and improve model performance. LangChain has an active community, and LLM developers use it to develop robust LLMs because of its flexibility and support. The ability of an AI reliability solution to integrate with LangChain makes it a handy choice for hallucination detection, as most of the LLM workflows are built using LangChain. 

LangChain Workflow with AI Reliability Solution

Wisecube’s Pythia: a Reliable Hallucination Detection Tool

Wisecube’s Pythia, an AI reliability solution, boasts the ten crucial characteristics of AI hallucination detectors. These characteristics set Pythia apart from other solutions by offering the following benefits:

• Enhanced reliability: Reduces the risk of AI errors using built-in hallucination detection and validators

• Trustworthy outputs: Builds trust in AI systems through continuously monitoring accurate and reliable outputs.

• Easy integration: Integrates into existing LangChain workflows, empowering developers to develop trustworthy AI systems.

• Customizable detection: Pythia can be configured to suit specific use cases, resulting in improved flexibility and increased accuracy. 

To learn more about the impact of hallucinations and the characteristics of an effective AI reliability solution, watch the webinar here.