Why can LLMs struggle with information in long contexts?

LLMs can struggle with information in long contexts because having more tokens available does not guarantee that the model will identify, retain, and use every relevant detail equally well.

As the context grows, important information has to compete with much more surrounding text.

1. Relevant information can become harder to find

A long context may contain thousands of tokens, but only a small part may matter for the current question.

The model must effectively locate that information among:

• Repeated details
• Unrelated passages
• Similar-looking facts
• Multiple instructions
• Large amounts of background text

This makes retrieval from the prompt more difficult.

2. Position can affect how well information is used

Information placed at different positions in a long context may not influence the model equally.

A known example is the lost-in-the-middle problem, where important information buried in the middle of a long sequence can sometimes be used less reliably than information near the beginning or end.

For that specific behavior, see “What is the lost-in-the-middle problem in LLMs?”

3. More context can create more competing signals

Long prompts often contain multiple facts, examples, or instructions.

If some of them are irrelevant, outdated, duplicated, or contradictory, the model must determine which information should guide the response.

More tokens can therefore add useful evidence, but they can also add noise.

4. Connecting distant information can be difficult

Some tasks require the model to combine facts that appear far apart in the sequence.

For example:

Fact A near the beginning
        ↓
thousands of tokens
        ↓
Fact B near the end
        ↓
combine A + B correctly

This can be more difficult than reasoning over information that appears close together.

5. Long-context capability depends on training

A model may support a large context window technically, but its ability to use that full range depends on how it was trained and evaluated.

A larger supported context does not automatically mean equally strong performance at every context length.

How can this be improved?

Applications can make long contexts easier to use by:

• Retrieving only relevant passages
• Removing duplicated or irrelevant text
• Structuring prompts clearly
• Placing important instructions prominently
• Summarizing older information
• Splitting complex tasks into smaller focused steps

The goal is to provide the model with relevant context, not simply the maximum possible amount of context.

In short: LLMs can struggle with long contexts because important information becomes harder to locate, position can matter, irrelevant text creates competition, and distant facts can be difficult to connect reliably.