The introduction of Large Language Models (LLMs) has brought in a significant paradigm shift in artificial intelligence (AI) and machine learning (ML) fields.
With their remarkable advancements, LLMs can now generate content on diverse topics, address complex inquiries, and substantially enhance user satisfaction. However, alongside their progression, a new challenge has surfaced: Hallucinations. This phenomenon occurs when LLMs produce erroneous, nonsensical, or disjointed text. Such occurrences pose potential risks and challenges for organizations leveraging these models. Particularly concerning are situations involving the dissemination of misinformation or the creation of offensive material.
As of January 2024, hallucination rates for publicly available models range from approximately 3% to 16% [1]. In this article, we will delineate various strategies to mitigate this risk effectively.
Contextual Prompt Engineering/Tuning
Prompt engineering is the process of designing and refining the instructions fed to the large language model to retrieve the best possible outcome. A blend of expertise and creativity is required to craft the best prompts to elicit specific responses or behaviors from the LLMs. Designing prompts that include explicit instructions, contextual cues, or specific framing techniques helps guide the LLM generation process. By providing clear guidance and context, GPT prompts engineering reduces ambiguity and helps the model generate more reliable and coherent responses.
Elements of a Prompt
- Context: Introducing background details or providing a brief introduction helps the LLM understand the subject and serves as a starting point for discussion.
- Instructions: Crafting clear and concise questions ensures that the model’s response stays focused on the desired topic.
- Input Examples: Providing specific examples to the model helps generate tailored responses.
- Output Format: Specifying the desired format for the response guides the LLM in structuring its output accordingly.
- Reasoning: Iteratively adjusting and refining prompts based on the model’s responses can significantly enhance output quality.
Positive Prompt Framing
It has been observed that using positive instructions instead of negative ones yields better results. Example of negative framing: Do not ask the user more than 1 question at a time. Example of positive framing: When you ask the user for information, ask a maximum of 1 question at a time.
Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is the process of empowering the LLM model with domain-specific and up-to-date knowledge to increase accuracy and auditability of model response. This is a powerful technique that combines prompt engineering with context retrieval from external data sources to improve the performance and relevance of LLMs.
Model Parameter Adjustment
Different model parameters, such as temperature, frequency penalty, and top-p, significantly influence the output created by LLMs. Higher temperature settings encourage more randomness and creativity, while lower settings make the output more predictable. Raising the frequency penalty value prompts the model to use repeated words more sparingly. Similarly, increasing the presence penalty value increases the likelihood of generating words that haven’t been used yet in the output.
Model Development/Enrichment
Fine tuning a pre-trained LLM involves training it with smaller, task-specific datasets to improve accuracy. Fully custom LLMs can be developed from the ground up for specific domains. Human oversight and user education are also crucial in mitigating hallucinations in LLMs.
Conclusion
The prevalence of hallucinations in Large Language Models (LLMs) poses a significant challenge despite various empirical efforts to mitigate them. While these strategies offer valuable insights, the fundamental question of complete elimination remains unanswered. Continued research and responsible AI usage are key in addressing hallucinations effectively.
I hope this article has shed light on hallucinations in LLMs and provided strategies for addressing them. Let me know your thoughts in the comment section below.
Reference: https://huggingface.co/spaces/vectara/leaderboard
