How big is the hallucination problem with long context?
Long context doesn't just fail to fix hallucinations—it can make them worse. When researchers compressed the key-value cache (a memory-saving technique) to handle longer inputs, hallucination scores jumped by up to 3.36 times compared to the baseline [3]. That's not a small slip; it's a tripling of errors in summarization tasks. The same study found that their mitigation method, HalluKV, could cut those hallucinations by an average of 5.48 points on a standard benchmark for one model, showing that the problem is both real and addressable [3].
Another line of research reveals a different kind of error that only emerges with long, multi-source context. In a controlled test of 272 cases, when the input contained conflicting information from different sources, the model misattributed facts to the wrong part of the context in 38.3% of cases—a phenomenon called 'Ghost Context' [7]. Across all scenarios, irrelevant distractor information influenced the model's output 20.4% of the time [7]. So roughly one in five times you give a model a long prompt with extra details, it will quietly use the wrong ones to shape its answer.
Why are these errors harder to notice than regular hallucinations?
The key difference is that long-context errors often look correct. A standard hallucination invents information that isn't in the source material—something a careful reader can catch by checking. But with Ghost Context, the model uses real information from the prompt; it just picks the wrong piece of evidence [5][7]. The output is factually true and supported by something in the input, so standard faithfulness checks that treat the whole context as one pool of evidence won't flag it [5]. As one paper puts it, 'the error is not fabrication; it is misattribution' [5].
This makes detection fundamentally harder. A 2025 study that built a specialized long-context hallucination detector found that even the best machine learning models struggled: Random Forest achieved 94.77% accuracy on test data but had a recall of only 4.85%—meaning it caught almost none of the actual hallucinations [2]. The authors note a severe trade-off between precision and recall, especially with imbalanced datasets [2]. Another approach using semantic entropy (measuring uncertainty at the level of meaning rather than word choice) showed promise for detecting confabulations without task-specific training, but it targets a different subset of errors [4]. So the tools we have for spotting hallucinations are less effective when the context is long and the errors are subtle.
Can we fix long-context hallucinations, or should we avoid long context?
There are promising fixes, but they require active intervention—not just hoping the model handles the extra information well. The Ghost Context research found that simply masking the single most influential distractor span resolved 95.5% of detected errors, with only 2.4% collateral damage and zero false positives on clean inputs [7]. That means the errors are 'causally localizable and cheaply correctable' once you know where to look [7]. Similarly, the HalluKV method selectively removes generated key-value pairs from retrieval heads during decoding, which reduced hallucination scores significantly across multiple models [3].
Another strategy is to use Retrieval-Augmented Generation (RAG), where the model retrieves only relevant chunks instead of processing everything at once. A unified evaluation framework called U-NIAH tested this across many scenarios and found that RAG achieved an 82.58% win rate over direct long-context answers [1]. However, RAG introduces its own problems: retrieval noise and chunk ordering can degrade performance, and under high noise conditions, hallucinations still occur [1]. A layered multi-expert framework tested in mental health assessments also reduced hallucinations by having smaller models handle subtasks before passing results to a long-context model [6]. The takeaway is that long context is usable, but you need to pair it with detection tools, retrieval systems, or multi-model architectures—not trust it on its own.
About These Sources
This answer is built on 7 peer-reviewed studies — published from 2024 to 2026, 7 from 2024 or later, 2 in Q1 journals, collectively cited 439 times — selected as the most relevant from 8 studies that passed quality screening, drawn from 49 papers retrieved from a database of over 500 million.
Sources used in this answer
U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-in-a-Haystack
In a unified evaluation of long-context LLMs and RAG, RAG achieved an 82.58% win rate over direct answers, but retrieval noise and chunk ordering caused omissions and hallucinations, especially under high noise conditions.
Detecting Hallucinations in Large Language Models Using Machine Learning Classifiers: A Comparative Study
Machine learning classifiers for hallucination detection showed a severe precision-recall trade-off: Random Forest had 94.77% test accuracy but only 4.85% recall, meaning it missed most actual hallucinations.
Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization
Aggressive KV cache compression for long-context summarization increased hallucination scores by up to 3.36×; the proposed HalluKV method reduced hallucinations by up to 5.48 average points on Llama-3-8B-Instruct.
Detecting hallucinations in large language models using semantic entropy
Semantic entropy, measuring uncertainty at the level of meaning rather than word sequences, detected confabulation-type hallucinations across tasks without task-specific training.
Ghost Context: Cross-Context Hallucination in Long-Context Language Models
Introduced 'Ghost Context' as a distinct hallucination type where models use information present in the context but from the wrong span, making errors invisible to standard faithfulness checks.
A Layered Multi-Expert Framework for Long-Context Mental Health Assessments
A layered multi-expert framework (SMMR) using multiple LLMs and smaller models reduced hallucinations and improved accuracy in long-context mental health assessments compared to single-model baselines.
Ghost Context: Measuring Cross-Context Interference in Long-Context Language Models
In a 272-case corpus, strict Ghost Context (factual misattribution) occurred in 38.3% of temporal conflict scenarios; masking the single highest-attributed distractor resolved 95.5% of errors with 2.4% collateral damage.