Long-Context Reality Check: How Fact Placement and “Don’t Make It Up” Prompts Change LLM Reliability
Table of Contents
- Introduction
- Why This Matters
- How Long-Context Really Works: Beyond the Needle
- The Experimental Setup: Fact Placement, Prompts, and Models
- Key Findings: Robustness, Cliffs, and the Safety Tax
- Practical Takeaways for Real-World AI Deployments
- Sources & Further Reading
Introduction
If you’ve ever pasted thousands of pages of documents into an AI prompt and hoped for grounded, trustworthy answers, you’re not alone. The new research on long-context LLMs digs into a stubborn question: does a bigger memory actually help when the “needles” (the relevant facts) are scattered across a long haystack? The study, Not All Needles Are Found: How Fact Distribution and Don’t Make It Up Prompts Shape Literal Extraction, Logical Inference, and Hallucination Risks in Long-Context LLMs, takes a scalpel to long-context performance. It splits the evaluation into literal extraction (pulling the exact facts present), logical inference (deriving conclusions from those facts), and hallucination risk (making stuff up). And it doesn’t stop at simple context-length tests; it introduces realistic distributions of evidence across the text and tests how anti-hallucination prompts actually reshape behavior.
This work builds on a growing chorus in the field about context length being a marketing headline more than a reliable operational metric. You can read the original paper here: Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Literal Extraction, Logical Inference, and Hallucination Risks in Long-Context LLMs. The authors extend the classic needle-in-a-haystack idea by making the “haystack” more realistic and by separating extraction from inference, which matters when you deploy LLMs in research and business workflows that hinge on credible grounding.
Why This Matters
Right now, in many enterprises, teams are leaning toward longer and longer context windows. The lure is clear: paste massive documents or databases into prompts and get grounded, up-to-date answers without the overhead of a retrieval system. But as the paper shows, a longer memory is not a free pass to better accuracy. In fact, when relevant evidence is diluted or dispersed, performance can dip, and in some cases, models become overly cautious because of anti-hallucination instructions.
Real-world relevance is high for several reasons:
- You’re likely to face long, messy documents in fields like law, research, or compliance. Facts are often not a single sentence but spread across chapters, footnotes, and cross-references. Understanding how models handle that distribution is crucial before trusting outputs as authoritative.
- The study highlights a practical risk: anti-hallucination prompts intended to curb fabrication can push models toward refusals or overly conservative answers, hurting literal extraction and inference.
- It contrasts different production models, showing that some handle long contexts with surprising resilience, while others hit performance cliffs. That matters when choosing tools for critical tasks or deciding whether to invest in RAG (retrieval-augmented generation) or cache-based grounding.
In short, this isn’t just a curiosity about memory capacity; it’s a guide to reliability in real-world AI-assisted decision-making. It also pushes beyond previous research that often relied on synthetic benchmarks. The authors’ use of Balzac’s La Comédie Humaine as a long-form, interconnected narrative provides a rich, naturalistic test bed for how facts appear and cluster in real writing. If you’re evaluating LLMs for enterprise grounding today, this paper helps you ask the right questions about distribution, prompt safety, and model robustness.
How Long-Context Really Works: Beyond the Needle
The core message is not simply “longer context equals better results.” It’s more nuanced: performance depends on how information is placed, how dense it is, and how a model’s architecture interprets distant clues. Here are two key ideas the paper centers on, explained in plain terms.
Literal extraction vs. logical inference
- Literal extraction is the straightforward task: find and reproduce facts that are explicitly present in the input. Think: exact quotes or unambiguous statements.
- Logical inference is more like a puzzle: given a set of facts in the text, what conclusions can be entailed by those facts, even if not stated verbatim? This requires combining pieces of evidence and sometimes performing multi-hop reasoning.
An analogy: if the haystack were a cookbook, literal extraction would be copying a recipe line exactly as written; logical inference would be figuring out that you can bake a cake by combining ingredients listed across several recipes, even if the cake isn’t described in a single sentence.
Positional biases and distributional collapse
- Positional biases are the model’s tendency to over- or under-weight information depending on where it appears in the prompt. Classic “lost-in-the-middle” effects show that information buried toward the middle can be ignored or forgotten as context grows.
- Distributional collapse is what happens when the evidence is not evenly spread but clustered in particular regions of the context. When that happens, even a model with a very large memory can miss crucial facts because attention, recall, or grounding become overwhelmed by the surrounding noise.
Together, these ideas predict a surprising outcome: longer context windows do not guarantee better performance, especially in real-world, uneven information landscapes. The study demonstrates this with four production-scale models and a battery of tests that explicitly vary where facts appear and how they’re distributed across the long input.
The Experimental Setup: Fact Placement, Prompts, and Models
The researchers set up a rigorous, multi-dimensional test to push long-context LLMs in directions that mirror real-world use. Here’s how they did it, in approachable terms.
Needle-in-a-haystack gets real
- Traditional needle-in-a-haystack (NIAH) tests hide a single fact in a long document. The authors extend this by introducing multiple facts (needles) and varying the layout of those facts across the context.
- They used a large, narratively rich corpus derived from Balzac’s La Comédie Humaine (public domain), totaling about 2 million tokens. That provides a more authentic texture than synthetic benchmarks.
Measuring across contexts and prompts
- They tested two prompting strategies: a Standard Prompt (just ask for the answer) and an Anti-Hallucination (AH) Prompt (Don’t Make It Up), which tells the model not to guess if the answer isn’t in the text.
- They measured three dimensions: Literal Extraction (did the model pull the explicit facts?), Logical Inference (could it reason over the facts to derive conclusions?), and Faithfulness (did the response stay truthful to the input, or did it slip into fabrication?).
- The evaluation used a deterministic decoding setup to reduce randomness, with a “Quiz” format of 30 questions tied to the narrative. The model was asked to answer as if they had read the story carefully, with strict output formatting to simplify automated grading.
The players
- Four production-scale models with different context window sizes:
- Gemini-2.5-flash: up to 1,000,000 tokens
- ChatGPT-5-mini: up to 272,000 tokens
- Claude-4.5-haiku: up to 175,000 tokens
- Deepseek-v3.2-chat: up to 128,000 tokens
- The authors report results for both Standard and Anti-Hallucination prompts, and they include a thorough look at how performance changes when you push context lengths toward the models’ maximum.
Key Findings: Robustness, Cliffs, and the Safety Tax
Capacity vs aggregate performance
- A striking split emerges when you push models to their maximum context: two models (Gemini-2.5-flash and Deepseek-v3.2-chat) stay remarkably stable. Their capacity performance nearly tracks their aggregate performance, sometimes even matching it in some metrics (e.g., Logical Inference for Gemini stays near 98% at capacity).
- Other models show clear strain at the context frontier. Claude-4.5-haiku loses ground on literal extraction at 175k tokens (68.0% capacity vs 78.7% aggregate). ChatGPT-5-mini experiences a pronounced drop under maximum context with AH prompts, especially in literal extraction (Aggregate 90.3% dropping to 72.0% capacity) and even more to 68.0% for logical inference.
Distributional robustness across distributions
- Beyond a single long document, the team tested how performance shifts when facts are distributed across the haystack according to nine probability distributions (Uniform, Normal, Exponential, etc.). This “Distributed Needle” setup is designed to mimic how real-world documents spread evidence.
- The results reveal a distributional fragility in several models. Notably, ChatGPT-5-mini can plunge to near-zero performance in literal extraction and logical inference when facts cluster in certain parts of the context under AH prompts. In contrast, Gemini-2.5-flash and Deepseek-v3.2-chat maintain strong performance across varied distributions, suggesting deeper robustness to how information is distributed rather than just how much context is used.
- The paper highlights a phenomenon called “Distributional Collapse” where performance collapses if evidence concentrates in a region that the model’s attention or safety checks deem less trustworthy. This is a critical warning for real-world deployments that deal with mixed-quality inputs.
Anti-hallucination prompts and the safety tax
- AH prompts are a double-edged sword. They reduce hallucinations but can trigger a “safety tax”—a measurable drop in legitimate extraction and inference when the model over-refuses or refuses to answer even when the evidence is present.
- The study finds that the safety tax is highly model-dependent. ChatGPT-5-mini shows the most pronounced tax, with substantial drops in literal extraction under AH prompts as context grows. Deepseek-v3.2-chat and Claude-4.5-haiku show a more nuanced mix: some gains in faithfulness, but not catastrophic losses in extraction.
- These dynamics suggest a practical takeaway: halting hallucinations through strict prompts may degrade core abilities to extract facts and reason, especially in long, dispersed contexts. That’s a real concern for teams relying on prompt-based grounding instead of retrieval systems.
Practical implications throughout
- The authors argue that the core issue isn’t simply whether facts exist in the input, but whether the model can identify, prioritize, and ground those facts across long contexts. Even with large context windows, many models struggle to identify the right signals when evidence is scattered, clustered in the middle, or spread thinly across pages of text.
- In enterprise settings, this means you can’t rely on long context alone to solve grounding problems. A well-designed RAG or CAG (cache-augmented generation) pipeline may still be preferable for ensuring reliability, even as LLMs become technically capable of processing long inputs.
Main Content Sections
How Long-Context Really Works: Beyond the Needle
- Literal extraction vs. logical inference
- Literal extraction is like copying a line from a document; logical inference is like building a conclusion from multiple clues elsewhere in the text. The study shows that the gap between these two tasks grows as context length increases, particularly for inference tasks that require multi-hop reasoning.
- Positional biases and distributional collapse
- The “lost-in-the-middle” effect gets worse as context grows. It’s not just about capacity; it’s about where the model pays attention. When evidence is evenly spread, models fare better; when it’s clustered or dispersed in tricky ways, accuracy drops, and failure modes become more predictable (or more dramatic).
The Experimental Setup: Fact Placement, Prompts, and Models
- Needle-in-a-haystack gets real
- The investigators place multiple facts across narrative chunks and use a graded context-contraction approach to mimic how a real reader would integrate long passages. They don’t just test if the needle exists; they test if the model can assemble a valid answer from dispersed facts.
- Measuring across contexts and prompts
- They compare Standard prompts with AH prompts, quantify literal extraction, inference, and faithfulness, and include a controlled decoding setup to keep comparisons fair.
- The players
- Gemini-2.5-flash, ChatGPT-5-mini, Claude-4.5-haiku, and Deepseek-v3.2-chat. The first two show strong capacity performance with minimal drop, while the latter two reveal more nuanced vulnerabilities under maximum context.
Key Findings: Robustness, Cliffs, and the Safety Tax
- Capacity vs aggregate performance
- Gemini-2.5-flash and Deepseek-v3.2-chat show near-parity between capacity and aggregate performance; others experience capacity-based drops, especially under AH prompts.
- Example numbers (illustrative): Gemini’s literal extraction sits around 98–99% at capacity, with logical inference in the high 90s; ChatGPT-5-mini’s literal extraction drops from mid-90s to about the high 70s or low 80s at full capacity under AH. Claude and Claude-like models show mid-range literal extraction and bigger dips in inference under max context.
- Distributional robustness across distributions
- The performance signatures reveal that some models are highly sensitive to how facts are arranged. For example, under Normal and Lorentzian distributions, ChatGPT-5-mini can crash to near-zero literal extraction with AH prompts, whereas Gemini-2.5-flash and Deepseek-v3.2-chat hold up much better.
- Anti-hallucination prompts and the safety tax
- The safety tax is not uniform across models. Some models become overly cautious, refusing valid queries and sacrificing accuracy; others gain faithfulness without a proportional hit to extraction. The practical implication: a one-size-fits-all anti-hallucination prompt strategy is risky in long-context use.
Practical Takeaways for Real-World AI Deployments
- Don’t assume longer context automatically fixes grounding issues
- If your documents are long and interconnected but poorly structured, a bigger prompt window won’t necessarily yield better grounding. In many cases, you’ll see diminishing returns or even worse performance, especially for inference tasks.
- Evaluate distributional robustness, not just capacity
- Real-world corpora are not uniformly structured. Distributional robustness—the model’s ability to identify and weigh dispersed evidence—matters more than just token limits. Tools like distributional testing can reveal whether a model will choke on clustered or sparse evidence.
- Rethink anti-hallucination prompts in context-rich tasks
- Prompts like Don’t Make It Up can curb hallucinations but may trigger safety tax that harms literal extraction and logical inference. If your workflow demands precise facts and inferences, you may want to tune or combine prompts with grounding approaches (e.g., retrieval systems) rather than rely solely on prompt-based safety.
- Model selection matters
- The paper’s findings suggest that some models (notably Gemini-2.5-flash and Deepseek-v3.2-chat) exhibit stronger robustness to long-context stress. If your use case depends on maintaining grounding as you scale context, model choice should weigh these robustness properties alongside cost and latency.
- Consider hybrid grounding strategies
- While long-context models are approaching RAG-like grounding in practice, purely relying on them for authority can be risky. The authors’ take: hybrid approaches—where a retrieval or cache layer anchors the evidence—still help ensure reliability at scale, especially in mission-critical settings.
Sources & Further Reading
- Original Research Paper: Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Literal Extraction, Logical Inference, and Hallucination Risks in Long-Context LLMs
- Authors:
- Amirali Ebrahimzadeh
- Seyyed M. Salili
For readers who want a deeper dive, the paper’s methodology, results, and discussion sections provide a careful, data-driven look at how long-context dynamics unfold in modern production-scale LLMs. It’s a timely reminder that, as we push toward longer prompts and bigger windows, we must also push for more nuanced evaluation frameworks that capture distributional effects, not just raw context length.
If you’re planning to deploy or benchmark long-context LLMs in your organization, start with a distribution-aware evaluation: test literal extraction and inference across multiple fact layouts, compare how prompts influence safety and usefulness, and weigh the value of grounding strategies beyond pure prompt design. The landscape is shifting quickly, but studies like this give concrete guardrails for making long-context AI more trustworthy in real-world tasks.
Note: For an accessible introduction to the core ideas—multihop reasoning, positional biases, and the balance between hallucination risk and factual recall—you can also explore related surveys and benchmarks cited in the paper. And of course, the original arXiv link above is part of the foundation for this evolving discussion.
