Table of Contents

What is Hallucination in Large Language Models?


TL;DR

Hallucination is when a language model generates text that is fluent and confident but factually wrong, fabricated, or unsupported by the input. Hallucinations are an inherent property of probabilistic text generation: models predict plausible next tokens, not verified facts. In LM-Kit.NET, hallucinations are mitigated through multiple strategies: RAG grounds responses in retrieved documents, grammar-constrained decoding forces structurally valid output, agent grounding connects models to real-world data via tools, and speech transcription includes a dedicated SuppressHallucinations mechanism on the SpeechToText class.


What is Hallucination?

Definition: A hallucination occurs when a language model produces output that appears correct but contains invented facts, fabricated references, nonexistent entities, or logical contradictions. The term borrows from psychology: just as a person experiencing a hallucination perceives something that is not there, an LLM "perceives" patterns in its training data that lead it to generate plausible but false content.

The Spectrum of Hallucination

Hallucinations range from subtle to severe:

Type Example Severity
Factual error "The Eiffel Tower was built in 1901" (actually 1889) Moderate
Fabricated citation Inventing a paper title, authors, and journal that do not exist High
Entity confusion Merging facts about two real people into one biography Moderate
Confident nonsense Generating plausible-sounding but meaningless technical jargon High
Contradicting the input Summarizing a document and stating the opposite of what it says Critical
Phantom knowledge Answering questions about events after the training cutoff as if they happened Moderate

Why Do LLMs Hallucinate?

Understanding the root causes helps choose the right mitigation strategy:

1. Probabilistic Generation

LLMs generate text by predicting the most likely next token given the preceding context. They optimize for fluency (what sounds right) rather than factuality (what is true). A statistically plausible continuation is not necessarily a correct one.

2. Training Data Limitations

Models learn from their training corpus. If the training data contains errors, contradictions, or gaps, the model absorbs those flaws. It cannot distinguish verified facts from plausible fiction in its training set.

3. Knowledge Cutoff

Every model has a training cutoff date. Questions about events after that date force the model to extrapolate, often producing confidently wrong answers.

4. Distributional Shift

When the input falls outside the patterns the model learned during training (e.g., a rare technical domain, an unusual language, or an ambiguous query), the model falls back to generating statistically plausible text that may have no factual basis.

5. Pressure to Respond

Most instruction-tuned models are trained to be helpful and provide an answer. This training bias makes models reluctant to say "I don't know," leading them to fabricate an answer rather than admit uncertainty.


Mitigation Strategies

Retrieval-Augmented Generation (RAG)

The most effective general-purpose mitigation. RAG retrieves relevant documents from a knowledge base and injects them into the model's context. The model generates responses grounded in the retrieved evidence rather than relying solely on its parametric knowledge.

using LMKit.Model;
using LMKit.Retrieval;
using LMKit.Embeddings;

var chatModel = LM.LoadFromModelID("gemma3:12b");
var embeddingModel = LM.LoadFromModelID("qwen3-embedding:0.6b");

// Ground responses in verified documents
var ragEngine = new RagEngine(chatModel, new Embedder(embeddingModel));
var dataSource = ragEngine.AddDataSource("verified-knowledge");
dataSource.ImportText("LM-Kit.NET was released in July 2024...");

// Model now generates from retrieved facts, not parametric memory

Why it works: The model sees the correct information in its context window and generates from it, dramatically reducing reliance on potentially incorrect memorized knowledge.

Grammar-Constrained Decoding

Force the model to produce structurally valid output by constraining generation to a formal grammar or JSON schema. This prevents structural hallucinations (e.g., inventing JSON keys, generating malformed data).

using LMKit.Extraction;
using LMKit.Model;

var model = LM.LoadFromModelID("gemma3:12b");
var extractor = new TextExtraction(model);

// Schema-constrained output: model MUST produce valid fields
extractor.Elements.Add(new TextExtractionElement("city", ElementType.String));
extractor.Elements.Add(new TextExtractionElement("population", ElementType.Integer));

Why it works: The model can only emit tokens that satisfy the grammar at every step. It cannot fabricate fields or produce syntactically invalid output.

Agent Grounding with Tools

AI agent grounding connects models to external data sources through tools. Instead of guessing, the agent retrieves live data.

using LMKit.Agents;
using LMKit.Agents.Tools.BuiltIn;

// Agent grounds answers in real web search results
var agent = Agent.CreateBuilder(model)
    .WithTools(tools =>
    {
        tools.Register(BuiltInTools.WebSearch);
        tools.Register(BuiltInTools.DateTime);
    })
    .WithSystemPrompt("Always search for facts before answering. Cite your sources.")
    .Build();

Why it works: The model calls tools to fetch current, verified information rather than relying on possibly outdated or incorrect parametric knowledge.

Speech Hallucination Suppression

In speech-to-text, hallucinations manifest as transcribed words or phrases that were never spoken (often during silence or noise). LM-Kit.NET's SpeechToText class includes a dedicated mechanism:

using LMKit.Speech;

var stt = new SpeechToText(whisperModel)
{
    SuppressHallucinations = true,    // Multi-signal adaptive filtering
    SuppressNonSpeechTokens = true    // Remove filler and noise artifacts
};

The hallucination suppression system combines five signals: audio energy analysis (RMS), statistical adaptation, no-speech probability scores from the model, token confidence bypass, and speaking rate validation.

Prompt Engineering

System prompts can reduce hallucinations by instructing the model to acknowledge uncertainty:

using LMKit.TextGeneration;

var chat = new MultiTurnConversation(model);
chat.SystemPrompt = """
    Answer questions accurately based on your knowledge.
    If you are not sure about something, say "I'm not certain about this."
    Never invent facts, citations, or statistics.
    When possible, qualify your confidence level.
    """;

Temperature and Sampling

Lower temperature values make the model more deterministic, reducing creative (and often hallucinatory) divergence. For factual tasks, lower temperature is generally safer.


Detecting Hallucinations

Technique How It Works LM-Kit.NET Approach
Cross-reference with RAG Compare generated claims against retrieved documents RagEngine.FindMatchingPartitions()
Confidence scoring Low model confidence suggests uncertainty Categorization.Confidence property
Perplexity monitoring High perplexity on generated tokens may indicate fabrication Token-level perplexity in TextGenerationResult
Structured extraction Schema validation catches structural hallucinations TextExtraction with required fields
Human review Route uncertain outputs for human verification Filters with completion validation

Key Terms

  • Hallucination: Model output that is fluent and confident but factually incorrect, fabricated, or unsupported by the input.
  • Grounding: Connecting model responses to verifiable external data to reduce hallucinations.
  • Factual Accuracy: The degree to which generated text corresponds to real-world facts.
  • Parametric Knowledge: Information encoded in a model's weights during training, as opposed to information retrieved at inference time.
  • Knowledge Cutoff: The date after which a model has no training data, making it prone to hallucinate about recent events.
  • Confabulation: A synonym for hallucination, emphasizing the model's tendency to fill knowledge gaps with plausible inventions.



External Resources


Summary

Hallucination is the fundamental reliability challenge of generative AI: language models produce text that sounds correct but may contain fabricated facts, invented references, or logical contradictions. This happens because LLMs optimize for fluency, not factuality. In LM-Kit.NET, hallucinations are mitigated through a layered approach: RAG grounds responses in retrieved documents, grammar-constrained decoding forces structurally valid output, agent grounding connects models to live data via tools, filters enable output validation, and SpeechToText.SuppressHallucinations provides specialized protection for audio transcription. Understanding when and why hallucinations occur is essential for building trustworthy AI applications.

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