Lost in the Middle Effect Quiz

Answers and explanations appear as you take the quiz.

1. What positional pattern did the lost-in-the-middle study report?
   • Performance improved steadily toward the center
   • Position did not affect performance
   • Only the first token could be retrieved
   • Performance was often better when relevant information appeared near the beginning or end
2. What was the study’s task setup?
   • Generate images from short captions
   • Find relevant information in long contexts for multi-document QA and key-value retrieval
   • Classify sentiment in one sentence
   • Translate a document without context
3. What does this effect imply about a large context window?
   • Raw window capacity does not guarantee even use of all included information
   • A larger window guarantees better reasoning
   • Anything inside the window has equal influence
   • The context limit can be ignored
4. A RAG prompt includes many retrieved passages. What should be evaluated?
   • Only the total number of passages
   • Whether the prompt is at maximum length
   • Whether the model uses evidence at different positions, not just whether it is present
   • The order of citations in the final paragraph
5. How can a position-controlled benchmark help?
   • Change the model and fact for every position
   • Move the same key fact through the context and measure performance at each location
   • Put the answer only at the beginning
   • Measure response fluency without checking correctness
6. Why might a relevant fact in the middle be missed?
   • The tokenizer removes all middle passages
   • Middle evidence is always contradictory
   • The model’s context window is necessarily exceeded
   • Its position can affect whether the model uses it, even when it is included
7. Which mitigation is suggested by the guide?
   • Add more irrelevant passages around it
   • Assume all models read every passage equally
   • Rerank important evidence toward a position where it is more likely to be used
   • Remove source links
8. What conclusion should not be drawn from this effect?
   • That every long-context model fails identically on every task
   • That position should be evaluated
   • That raw token limits do not guarantee equal use
   • That retrieval order may matter