What type of study is this?

This is a Quantitative Study study.

October 16, 2025Open Access

Positional Biases Shift as Inputs Approach Context Window Limits

View Full Paper

Ask AI

Bookmark

Authors

Comprehensive analysis reveals how positional biases shift depending on context window limits in LLMs.

The study shows that the Lost in the Middle effect is strongest when inputs occupy up to 50% of a model's context window.
Findings indicate that beyond this length, the primacy bias weakens while recency bias remains stable.
Relative input lengths are critical in understanding biases, revealing distance-based performance improvement for relevant information.
The results highlight the importance of successful retrieval in reasoning tasks for large language models.

Veseli et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark

October 16, 2025Open Access

View Full Paper

Ask AI

Bookmark

Authors

Comprehensive analysis reveals how positional biases shift depending on context window limits in LLMs.

The study shows that the Lost in the Middle effect is strongest when inputs occupy up to 50% of a model's context window.
Findings indicate that beyond this length, the primacy bias weakens while recency bias remains stable.
Relative input lengths are critical in understanding biases, revealing distance-based performance improvement for relevant information.
The results highlight the importance of successful retrieval in reasoning tasks for large language models.

Veseli et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark