AI Hallucinations: Why Language Models Still Lie and What Is Being Done About It

The Confidence of the Confidently Wrong

In spring 2023, a New York lawyer submitted a legal brief containing six case citations generated by ChatGPT. None of the cases existed. The citations were syntactically perfect — correct court names, plausible docket numbers, realistic legal reasoning — but entirely fabricated. The lawyer, Steven Schwartz of Levidow, Levidow & Oberman, was sanctioned by Judge P. Kevin Castel in Mata v. Avianca, Inc. — each attorney fined $5,000 and required to notify every judge falsely cited. The incident became a symbol of AI’s most dangerous failure mode: hallucination.

A hallucination occurs when a large language model generates information that is fluent, confident, and wrong — not a typo or an uncertainty, but a manufactured fact presented with the same conviction as a verified truth. The model does not “know” it is hallucinating. It has no internal mechanism for distinguishing between what it has memorized from training data, what it has inferred plausibly, and what it has invented wholesale.

In 2026, despite massive investment in mitigation techniques, hallucination remains the single largest barrier to trusted AI deployment in high-stakes domains: healthcare, law, finance, government, and journalism. Understanding why hallucinations happen — and what the current state of the art in mitigation looks like — is essential for any organization deploying LLMs.

Why Do LLMs Hallucinate? The Fundamental Architecture Problem

Hallucination is not a bug that can be patched. It is an emergent property of how language models work.

An LLM is a next-token prediction engine. Given a sequence of tokens (words, subwords), it predicts the statistically most likely next token based on patterns learned from its training data. It does not “look up” facts in a database. It does not “verify” claims against a source of truth. It generates text that is statistically plausible given the context, and statistical plausibility is not the same as factual accuracy.

Three specific mechanisms drive hallucination:

Training data gaps. When a model is asked about a topic that was sparsely represented in its training data — a rare legal precedent, a niche scientific finding, a recent event — it fills the gap with plausible-sounding confabulation rather than admitting ignorance. This is because the model’s training objective (minimize prediction loss) penalizes silence more than confident fabrication.

Compression artifacts. A model with 70 billion or even 1 trillion parameters cannot memorize the internet. It learns compressed statistical representations of its training data. When asked to recall specific facts — exact dates, precise numbers, correct citations — the compression introduces errors, similar to how a heavily compressed JPEG image loses detail.

Sycophancy and instruction-following pressure. Models fine-tuned with reinforcement learning from human feedback (RLHF) are optimized to produce responses that human raters prefer. Human raters generally prefer confident, detailed, helpful answers over hedged, uncertain, or incomplete ones. This creates an incentive for models to generate a definitive-sounding answer even when the correct response would be “I’m not sure” or “I don’t have reliable information about that.”

The Scale of the Problem in 2026

Hallucination rates have improved significantly since 2023, but they remain material for enterprise deployment:

General factual queries: Leading models (GPT-5, Claude Opus 4.6, Gemini 3.1 Pro) hallucinate on approximately 3-8% of general factual questions in controlled evaluations. On standardized benchmarks with grounding, top models like Gemini 2.0 Flash achieve rates as low as 0.7-1.5%. However, rates vary enormously by task type: legal questions still see hallucination rates of 6%+ even from top models, and complex reasoning tasks can produce error rates of 30-50%. The improvement from 2023 — when models like GPT-3.5 showed hallucination rates near 40% and GPT-4 around 29% — is substantial but uneven.

Long-form generation: Hallucination rates increase significantly in long documents. A 2,000-word AI-generated report may contain 2-5 factual errors that are invisible without expert review. These errors tend to be the most dangerous kind: small, specific, plausible, and embedded in otherwise accurate text.

Citation and reference generation: Despite improvements, models remain unreliable at generating accurate bibliographic references. A GPTZero analysis of ICLR 2026 submissions found over 50 hallucinated citations in approximately 300 scanned papers, while a scan of NeurIPS 2025 accepted papers found over 100 fabricated references out of 4,841 papers examined. Separately, the HalluCitation study (January 2026) analyzed 300 hallucinated papers found in ACL conference proceedings from 2024-2025. Citation fabrication rates have dropped significantly from the 40%+ seen in 2023, but remain a serious concern for scholarly and legal use.

Domain-specific hallucination: Models hallucinate at higher rates in specialized domains where training data is sparse: rare medical conditions, niche legal jurisdictions, emerging technologies, and non-English language contexts. This disproportionately affects users in regions and languages underrepresented in training data — including Arabic, which remains significantly underrepresented relative to English.

Mitigation Techniques: The State of the Art

Retrieval-Augmented Generation (RAG)

RAG is the most widely deployed hallucination mitigation technique in enterprise AI. Instead of relying solely on the model’s parametric memory, RAG systems retrieve relevant documents from a verified knowledge base and provide them as context for the model’s response. The foundational architecture was described by Lewis et al. (2020) at NeurIPS 2020.

The architecture is: user query → retrieval system searches a curated document corpus → top-k relevant documents are injected into the model’s context window → model generates a response grounded in the retrieved documents.

RAG dramatically reduces hallucination for questions answerable by the document corpus. Implementations have been shown to reduce hallucination rates by 40-70%, with some production deployments reporting even larger improvements — including one enterprise system that reduced source hallucinations from 10% to effectively 0% using Anthropic’s Citations API. However, RAG introduces its own failure modes: the retrieval system may return irrelevant documents (leading to “grounded but wrong” responses), the model may ignore the retrieved context and rely on parametric memory anyway, and questions that fall outside the corpus still trigger hallucination.

Grounding and Attribution

Grounding systems require the model to cite its sources explicitly — not just generate an answer, but point to the specific passage in the retrieved document that supports each claim. Google’s Gemini API supports grounding with Google Search and enterprise web search, while Anthropic’s Claude Citations API (launched January 2025) provides document-level source attribution that chunks source documents into sentences and cites specific passages.

Attribution enables verification: a user (or automated system) can check whether the cited source actually supports the claim. This does not prevent hallucination, but it makes hallucination detectable — transforming an invisible error into a verifiable one.

Constitutional AI and RLHF Refinement

Anthropic’s Constitutional AI (CAI) approach trains models to self-critique and revise their own outputs based on a set of principles, including factual accuracy. Models trained with CAI exhibit lower hallucination rates because they are more likely to hedge uncertain claims, say “I don’t know,” and flag when they are operating outside their reliable knowledge.

RLHF fine-tuning has also been refined to reward honest uncertainty rather than confident fabrication. Models in 2026 are measurably better at calibration — when they express high confidence, they are more likely to be correct; when they express uncertainty, they are more likely to be in error territory.

Chain-of-Thought and Self-Verification

Prompting models to reason step-by-step (chain-of-thought prompting) and then verify their own reasoning reduces hallucination on reasoning-intensive tasks. The model is more likely to catch logical errors when it is forced to show its work.

Self-verification pipelines go further: after the model generates an answer, a second pass (sometimes using a different model or the same model with a verification-focused prompt) checks the answer for factual consistency, internal contradictions, and unsupported claims. This adds latency and cost but meaningfully reduces error rates.

Structured Output and Constrained Generation

For tasks where the output format is well-defined (JSON, SQL, structured reports), constrained generation techniques force the model to produce output conforming to a schema. This eliminates a category of hallucination where models invent field values, misformat data, or generate syntactically invalid output.

The Industry Response: Trust Infrastructure

Beyond technical mitigation, the AI industry is building what might be called “trust infrastructure” — organizational and process-level safeguards around AI output:

Human-in-the-loop review remains the gold standard for high-stakes applications. AI generates a draft; a human expert reviews it. This is the model used in healthcare (AI suggests diagnoses, physician confirms), legal (AI drafts contract analysis, lawyer reviews), and journalism (AI generates initial research, editor fact-checks).

Automated fact-checking pipelines use external knowledge bases (Wikipedia, Wikidata, domain-specific databases) to automatically verify claims in AI-generated text. Tools like FActScore provide fine-grained atomic evaluation of factual precision, breaking AI-generated text into individual claims and verifying each one. These systems can flag potentially hallucinated statements for human review, reducing the burden on human reviewers.

Confidence scoring assigns a reliability score to each claim in an AI response, based on the model’s internal certainty metrics and external verification. Claims below a confidence threshold are flagged or withheld.

Audit trails log the full context of each AI generation — the prompt, the retrieved documents, the model version, and the generated output — enabling post-hoc investigation when errors are discovered.

The Honest Assessment: Hallucination Will Not Be “Solved”

It is important to state clearly: hallucination will never be fully eliminated from language models as currently architected. Next-token prediction with compressed representations will always produce some factual errors. The goal is not zero hallucinations but a hallucination rate low enough — and a detection rate high enough — that AI systems can be trusted within defined boundaries.

The models of 2026 are dramatically more reliable than those of 2023. RAG + attribution + self-verification pipelines can push hallucination rates below 1% for well-defined enterprise use cases with curated knowledge bases. But the long tail of edge cases, rare queries, and adversarial inputs will continue to produce failures.

The organizations that succeed with AI in 2026 are those that design for the presence of hallucinations — with verification layers, human review checkpoints, and clear escalation paths — rather than those that assume AI output is inherently trustworthy.

Sources

• Mata v. Avianca — Lawyer Sanctioned for AI-Generated Fake Citations
• GPTZero — Hallucinated Citations in ICLR 2026 Submissions
• HalluCitation — Analyzing Citation Hallucination in ACL Papers
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — Lewis et al.
• Constitutional AI: Harmlessness from AI Feedback — Anthropic
• Survey of Hallucination in Natural Language Generation — Ji et al.
• Google — Grounding with Google Search in Gemini API
• Anthropic — Claude Citations API
• FActScore — Fine-Grained Atomic Evaluation of Factual Precision