To detect hallucinations in RAG-generated answers, developers can use techniques that compare the generated output against the retrieved source texts to verify factual consistency. Three primary methods include semantic similarity checks, entailment verification, and entity/relation validation. These approaches focus on ensuring every factual claim in the answer aligns with evidence from the retrieved documents. For example, if a RAG model claims “The Earth’s core is 6000°C,” developers can cross-reference this with the retrieved sources to confirm whether the temperature value and context match.
A practical way to implement this is by breaking the generated answer into individual claims and comparing each against the retrieved text. For semantic similarity, tools like sentence transformers (e.g., SBERT) can encode both the claim and source text into vectors, then compute cosine similarity scores. Claims with low similarity scores (e.g., below 0.7) could indicate hallucinations. For entailment verification, models like Google’s T5 or BERT-based Natural Language Inference (NLI) models can classify whether a claim is supported (entailed), contradicted, or unrelated to the source. For example, if the generated text states “Study X found a 30% increase in efficiency,” but the source only says “Study X observed improved efficiency,” the entailment model would flag this as unspecific or unverified.
Developers can also use named entity recognition (NER) and relation extraction to validate entities (e.g., people, dates) and their relationships. Libraries like spaCy or Stanza can extract entities from both the answer and source text, enabling direct comparison. For instance, if the answer mentions “Dr. Smith conducted the 2023 trial,” but the source text only references “a 2021 study by researchers,” the mismatch in entities and dates would be flagged. Tools like FAISS or Annoy can index retrieved documents for fast similarity searches, while custom scripts can automate claim-source alignment. Combining these techniques into a pipeline—segmenting answers, retrieving relevant source snippets, and applying verification models—provides a systematic way to detect and reduce hallucinations in RAG outputs.
