Computer Vision Meetup: Reducing Hallucinations in ChatGPT and Similar AI Systems

Introduction

In this article, we’ll discuss the topic of hallucinations, which are inaccuracies that can arise in large language models (LLMs) such as ChatGPT. We will explore methods of reducing these hallucinations, specifically by utilizing knowledge graphs. The discussion is informed by a talk given by Manu from SED AI Factory in Europe.

Understanding Hallucinations

Hallucinations in the context of LLMs refer to instances where the model generates content that is incorrect, nonsensical, or not based on real facts. Broadly, we can categorize hallucinations into two main types:

1. Factual Errors: Instances where the model produces incorrect information about real-world facts, such as asserting that the Earth revolves around the Moon.

2. Inconsistent Responses: Situations where the model gives contradictory answers to the same prompt when queried multiple times.

Why Do LLMs Hallucinate?

There are several factors that contribute to hallucinations in LLMs:

1. Temperature Settings: This parameter controls the creativity of responses; lower temperatures may yield grounded answers, while higher settings can lead to more innovative but potentially inaccurate results.

2. Missing Information: LLMs may not have access to the most recent data if they were trained on information available only up to a certain date.

3. Model Complexity: The intricate nature of LLMs can make it challenging to trace errors, as they often learn from misleading or incorrect information from various sources.

Methods to Reduce Hallucinations

To combat hallucinations, various methods can be employed:

1. Prompt Engineering: Formulating well-structured prompts can lead to better responses.

2. In-Context Learning: Providing the model with relevant examples during prompting can improve accuracy.

3. Fine-Tuning: This involves retraining a model on a specific dataset but can be resource-intensive.

4. Grounding: Connecting the LLM to real-time data sources or databases can improve the factual basis of its responses.

Utilizing Knowledge Graphs

Knowledge graphs, which represent entities and their interrelations through nodes, edges, and labels, can be utilized to improve the output of LLMs significantly. They provide a structured approach to information, enabling LLMs to derive logical conclusions and enhancing their reasoning capabilities. Here are four key ways knowledge graphs can aid in reducing hallucinations:

1. Providing Factual Grounding: Knowledge graphs possess structured representations that help LLMs generate factually correct answers based on accurate relations and attributes.

2. Improving Reasoning Capabilities: By understanding relationships between different entities, LLMs can draw more logical conclusions.

3. Enhancing Information Retrieval: LLMs can efficiently access relevant data, mitigating the risks of providing incorrect context.

4. Reducing Over-Reliance on Statistical Patterns: Knowledge graphs can encourage LLMs to think beyond mere statistical associations found in the training data.

Practical Application

To demonstrate the application of knowledge graphs, the speaker introduced a simple running example using Neo4j, a graph database library. Using Neo4j in tandem with LangChain, they illustrated how to build a basic retrieval-augmented generation (RAG) application. The process involved querying movies based on user questions, allowing the model to look up relevant information in the graph database and provide accurate responses.

The discussion also highlighted the necessity of structuring prompts properly to generate reliable queries while handling potential variability in responses. Employing instruction templates or exemplifying query syntaxes can guide LLMs toward generating consistent and reliable outputs.

Future Exploration

Looking ahead, there is significant potential in utilizing LLMs to generate knowledge graphs from unstructured data. Projects like Neo4j's NM initiative aim to extract nodes and relationships from various data sources, paving the way for richer, contextual, and accurate interactions. Another innovative approach is Graph RAG from Microsoft, which employs LLMs to build knowledge graphs with enriched semantic representations.

Conclusion

Knowledge graphs present a promising avenue for improving the performance of LLMs, enhancing their reliability, and reducing the occurrence of hallucinations while delivering contextual relevance in conversational AI systems.

Keywords

• Hallucinations
• Large Language Models (LLMs)
• Knowledge Graphs
• Prompt Engineering
• Grounding
• Neo4j
• Retrieval-Augmented Generation (RAG)

FAQ

Q1: What are hallucinations in large language models?
A1: Hallucinations refer to inaccurate, nonsensical content generated by LLMs, characterized by factual errors or inconsistent responses.

Q2: How can knowledge graphs help reduce hallucinations?
A2: Knowledge graphs provide structured representations of entities and their relationships, improving factual grounding, reasoning, and information retrieval for LLMs.

Q3: What is prompt engineering?
A3: Prompt engineering involves crafting well-structured questions or prompts that guide LLMs to provide better, more accurate answers.

Q4: What is the role of temperature in LLM responses?
A4: The temperature setting influences the creativity of an LLM’s responses. Lower temperatures yield more grounded answers, while higher settings promote innovative but potentially inaccurate outputs.

Q5: What is RAG (Retrieval-Augmented Generation)?
A5: RAG is a technique that connects LLMs to real-time data sources, allowing them to access external information to generate more accurate and grounded responses.