The so-called “Lost‐in‐the‐Middle” phenomenon—where information in the middle of long inputs is less reliably used—remains a persistent limitation.

Why Large Language Models Struggle with Extracting Key Information from Large Data, and the Consequences for Trained Interpretation

by ChatGPT-4o

1. Technical Constraints: Context Limitations & Degradation Over Length

Context window limits impose fundamental boundaries. Even for models trained with very long windows (e.g. 128K tokens), the effective utilization is often much shorter. Research shows that open‑source models typically use less than half their training context length, sometimes only 64K effectively, due to skewed training exposure to long-range positions.

Moreover, models often exhibit positional biases, such as “primacy” (better recall of information at the start) or “recency” (better at the end). The so-called “Lost‑in‑the‑Middle” phenomenon—where information in the middle of long inputs is less reliably used—remains a persistent limitation.

This means that as you feed more data into an LLM, the later or mid‑section information may be overlooked or underweighted, making it hard for the model to surface the important elements unless they’re conveniently positioned.

2. Attention Mechanics & Cognitive Signals

Transformers, which power LLMs, use attention to weigh input tokens. However, attention scales quadratically with sequence length—a computational burden. To mitigate this, models often shift to more efficient architectures (e.g., Mamba layers) or sparse attention mechanisms, which may preserve token information but weaken the model’s ability to integrate long-range dependencies effectively.

Even when efficient attention is in place, researchers note that LLMs struggle to maintain coherence and meaningful integration across long segments—especially if the input structure is poor or lacks clear segmentation for what’s important.

3. Inherent Distractibility & Surface‑Level Processing

LLMs are vulnerable to distraction. Studies show that when irrelevant or tangential content is injected—particularly in problem-solving contexts—the model’s performance drops significantly. Without explicit guidance, LLMs may focus on superficial features rather than the core substance of the data.

Thus, a prompt overflowing with data risks overwhelming the model, leading it to latch onto spurious cues or obvious token patterns rather than distilling the meaningful insights.

4. Limits on Deep Reasoning & Semantic Understanding

LLMs excel at generating coherent text—but when it comes to extracting deep, abstract or creative connections, they fall short. From formal semantic limitations (e.g., inability to reliably capture entailment or consistency beyond simple levels) to domain‑specific reasoning gaps, LLMs are constrained in how deeply they can internalize complex relationships.

Also, creativity and novel insight generation are limited; often, outputs can seem repetitive or uninspired—especially when not guided by structured prompts or human direction.

5. Mitigation & Architectural Remedies

Researchers are exploring solutions:

• Iterative reasoning with summarization (e.g., INFTYTHINK) breaks down large reasoning tasks into manageable segments, summarizing intermediate steps to build deeper conclusions while staying within context limits.

• Chain-of-Agents frameworks, where multiple LLMs work collaboratively—each handling parts of a long context and passing summaries—yield better performance on long-context tasks than monolithic models.

• Retrieval-augmented generation (RAG) assists by pulling in only relevant snippets from external sources rather than feeding entire large inputs into a model, allowing more focused response generation.

Reflection: Implications for Interpretation of Trained Data

If LLMs, when provided extensive prompt or upload data, struggle to identify the most important aspects or form creative connections, what does this mean for how they interpret internal training data?

1. Training datasets are likewise massive, and the same contextual and attention limitations imply that some patterns or relationships receive less effective exposure. Important patterns that occur deep in long sequences may be under-trained, resulting in weaker internal representation.

2. Bias toward recency/primacy in training: Models might overweight parts of text that appear early or late in training examples. Critical content buried in mid-text may be less influential, leading to skewed or incomplete learning.

3. Creative or high-level abstractions may be poorly encoded. Unless the dataset repeatedly reinforces certain abstract connections across contexts, the model may fail to capture deeper conceptual relationships, instead relying on surface-level correlations.

4. Difficulties in generalization: If the model hasn’t robustly learned deep semantics or reasoning patterns due to these constraints, it may struggle to extend its understanding to novel or less‑typical prompts—even when similar information appeared during training.

5. Hallucination risk: When the model’s internal representation is insufficiently structured or incomplete, responses may drift or fabricate details, especially under vague or data-heavy prompts.

Conclusion

In sum, there are several structural and cognitive constraints that cause LLMs to struggle with extracting key insights from large or poorly structured input:

• Context window limitations and uneven attention,

• Architectural inefficiencies and attention sparsification,

• Distractibility and surface-level focus,

• Theoretical reasoning and semantic limits.

The same forces likely apply during training, meaning that certain patterns in pre-training data may be underrepresented or underutilized by the model.

Consequences include weaker performance on deep reasoning tasks, bias in learned patterns, reduced creativity, and an increased risk of hallucination or misinterpretation when deployed.

📚 Bibliography

1. Why Large Language Models Struggle with Long Contexts
   Understanding AI Substack

2. Why Does the Effective Context Length of LLMs Fall Short?
   arXiv preprint (2024)
   https://arxiv.org/html/2410.18745v1

3. Lost in the Middle: How Language Models Use Long Contexts
   arXiv preprint (2023)
   https://arxiv.org/abs/2307.03172

4. Positional Biases Shift as Inputs Approach Context Window Limits
   arXiv preprint (2025)
   https://arxiv.org/abs/2508.07479

5. Why I’m Not Worried About LLMs’ Long Context Problem
   Medium article by Social Scholarly
   https://medium.com/@socialscholarly/why-im-not-worried-about-llms-long-context-problem-eed21db44687

6. Large Language Models Can Be Easily Distracted by Irrelevant Context
   arXiv preprint (2023)
   https://arxiv.org/abs/2302.00093

7. Limits for Learning with Language Models
   arXiv preprint (2023)
   https://arxiv.org/abs/2306.12213

8. A Critical Assessment of Creativity in LLMs
   ScienceDirect – Journal of AI Research (2025)
   https://www.sciencedirect.com/science/article/pii/S3050741325000175

9. INFTYTHINK: Breaking the Length Limits of Long-Context Language Models via Hierarchical Summarization
   arXiv preprint (2025)
   https://arxiv.org/pdf/2503.06692

10. Chain-of-Agents: Large Language Models Collaborating on Long-Context Tasks
    Google DeepMind / Google Research Blog (2025)
    https://research.google/blog/chain-of-agents-large-language-models-collaborating-on-long-context-tasks/

11. Retrieval-Augmented Generation
    Wikipedia entry
    https://en.wikipedia.org/wiki/Retrieval-augmented_generation