Language models (LLMs) serve as potent tools, yet they possess the capability to generate inaccurate information or hallucinate. Hallucination is an inherent feature of LLMs, and the sole method to prevent it is by employing an external solution, such as Ariglad, instead of relying solely on the LLM itself.
Approximately a year ago, the term "large language models" (LLMs) wasn't commonly known among most individuals.
Nowadays, encountering someone unfamiliar with the LLM acronym is becoming increasingly rare. According to an IBM survey, approximately 50% of CEOs are considering incorporating generative AI into their services and products.
LLMs have evolved into potent tools, effortlessly addressing some of our most challenging queries. Interestingly, certain companies provide their employees with a ChatGPT Plus subscription.
Despite their usefulness, LLMs have a notable drawback – a tendency to "hallucinate" and potentially misguide individuals. Surprisingly, a Tidio survey reveals that 72% of users trust LLMs to deliver reliable and truthful information.
If we fail to address the issue of AI hallucinations, it could lead to significant consequences.
This article will delve into the causes of hallucination and explore strategies to prevent hallucinations in LLMs.
What Constitutes an LLM Hallucination?
Large Language Models (LLMs) such as ChatGPT, Llama, Cohere, and Google Palm exhibit a phenomenon known as "hallucination." When LLMs experience hallucination, they produce responses that are grammatically accurate and linguistically coherent.
However, these responses may be factually incorrect or nonsensical.
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To illustrate, ChatGPT falsely accused a professor of sexual harassment and referenced a non-existent Washington Post article.
In another instance, a lawyer utilized ChatGPT to draft a court filing, which included references to fictional court cases.
Understanding the Origins of LLM Hallucinations
To grasp how to prevent hallucinations in LLM, it's crucial to delve into their root causes. Here are some factors contributing to language model hallucination:
Repetition of Inaccuracies in Training Data
- When inaccuracies are present in the training data, the LLM may replicate these inaccuracies in its responses.
For instance, during its launch, Google's Bard falsely claimed that the James Webb Space Telescope was the first to capture images of planets beyond our solar system. (This statement is factually incorrect, likely stemming from an error in the training data.)
Lack of Fiction-Fact Distinction
- LLMs struggle to differentiate between fiction and fact, particularly when exposed to diverse sources. This challenge arises during the generation of responses.
Insufficient Context in Prompts
- Inaccurate or vague prompts can lead to erratic behavior in a language model. When fed ambiguous prompts, the LLM may generate responses that are unrelated or incorrect due to a lack of context.
Limited Domain-Specific Training
- While ChatGPT and its underlying LLMs (GPT-3.5 or GPT-4) utilize training data from the entire public internet, they may lack specific training for domains like finance, medicine, and law. Without extensive domain-specific knowledge, LLMs are more prone to hallucinate in these areas.
Probability-Based Response Generation
- LLMs function by analyzing vast amounts of text and predicting the next most probable word in a conversation, rather than serving as encyclopedic databases of facts. The challenge lies in the model's inability to assess the accuracy of its responses.
Addressing LLM Hallucinations: Mitigation Strategies
Now that we've examined the origins of LLM hallucination, you might be curious about ways to prevent or at least minimize these occurrences.
Prevention Challenges
- While an outright prevention of LLM hallucinations may not be feasible, there are avenues for reducing their frequency.
Focus on Mitigation: Instead of aiming to completely halt LLM hallucinations, consider strategies for mitigation.
Custom LLM Development
- One approach involves building a fully custom LLM tailored to a specific domain. Training the model exclusively on accurate and relevant knowledge enhances its understanding of subject-specific relationships and patterns.
- Caveats: Building a custom LLM is prohibitively expensive, and even with customization, some level of hallucination may persist.
Fine-Tuning a Pre-trained LLM
- Another option is fine-tuning a pre-trained LLM on a smaller dataset designed for a specific task. This process demands time, financial resources, and expertise in machine learning.
- Limitations: While fine-tuning can diminish hallucination, it cannot eliminate it entirely.
Retrieval Augmented Generation (RAG)
- Recognizing that complete elimination of LLM hallucinations is unattainable, a more effective strategy lies outside the LLM itself. Consider utilizing or constructing a chatbot employing Retrieval Augmented Generation (RAG).
- RAG Technique: This approach enhances the context around questions by:
- Generating embeddings for all knowledge using the LLM.
- Creating embeddings for the question or prompt with the LLM.
- Performing a similarity search across the knowledge base to identify the most pertinent information.
- Providing both the question and the most relevant knowledge as context to the LLM, minimizing hallucination risks.
