In this guide, we will explore how to address six common types of hallucinations in small language models using a 1 billion parameter (1B) LLaMA model. Hallucinations—errors where the model generates information that contradicts facts, context, or common sense—are a significant challenge in text generation. By understanding and applying the techniques discussed here, you can improve the reliability and accuracy of your own small language models. Our 1B LLM and Embedding Model We will use a 1B-sized LLaMA model for generating text, along with an appropriate embedding model to help contextualize the data. This combination allows us to tackle various issues effectively while maintaining the model's efficiency. Factual Correction Using RAG One of the most common hallucinations is factual inaccuracies. Retrieval-Augmented Generation (RAG) helps by integrating external knowledge sources into the model's output process. When the model generates text, RAG queries a database of relevant documents to ensure the generated content aligns with verified facts. This method enhances the model's accuracy by grounding its responses in reliable information. Temporal Correction Using Time-Aware Prompting Another frequent issue is temporal inconsistency, where the model may generate outdated or inaccurate information based on the time context. Time-aware prompting involves providing the model with specific timestamps or time frames to guide its generation. For example, if you want to discuss current events, include the present date in your prompt to ensure the model's output is up-to-date and relevant. Contextual Issues Using Lookback Lens Contextual hallucinations occur when the model fails to maintain a coherent narrative or forgets details from earlier parts of the conversation. The Lookback Lens technique addresses this by allowing the model to "look back" at previous inputs and outputs. This ensures that the model stays consistent and remembers key details throughout the conversation, maintaining a logical flow. Linguistic Issues Using Semantic Coherence Filtering Linguistic hallucinations can result in grammatically incorrect or nonsensical sentences. Semantic Coherence Filtering helps by evaluating the generated text against predefined linguistic rules and standards. This filtering process ensures that the final output is not only factually correct but also grammatically sound and coherent. Intrinsic Issues Using Contradiction Checking Intrinsic hallucinations happen when the model generates self-contradictory statements. Contradiction Checking involves analyzing the generated text to identify and resolve any internal inconsistencies. By doing so, the model produces more reliable and logically consistent content, which is crucial for maintaining trust and accuracy. Extrinsic Issues Using Copy/Pointer Mechanism Extrinsic hallucinations involve the model generating content that is unrelated or out of context with the input. The Copy/Pointer Mechanism helps by allowing the model to refer directly to or "copy" parts of the input text. This ensures that the generated content remains relevant and aligned with the provided context, making the output more useful and accurate. Conclusion Addressing hallucinations in small language models is essential for improving their reliability and effectiveness. By implementing the techniques of Factual Correction Using RAG, Temporal Correction Using Time-Aware Prompting, Contextual Issues Using Lookback Lens, Linguistic Issues Using Semantic Coherence Filtering, Intrinsic Issues Using Contradiction Checking, and Extrinsic Issues Using Copy/Pointer Mechanism, you can significantly reduce the occurrence of these errors. This guide provides a practical and conceptual framework for tackling each type of hallucination, enabling you to enhance the performance of your own 1B-sized LLaMA model or any similar small language model. With these strategies, your generated text can be more accurate, coherent, and trustworthy, ultimately delivering better results and user experiences.