Summary of Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization, by Cheng-yu Hsieh et al.
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization
by Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the “lost-in-the-middle” problem in large language models (LLMs), where they struggle to capture relevant information located in the middle of their input context. The authors identify a connection between this phenomenon and LLMs’ intrinsic attention bias, which favors tokens at the beginning and end of the input over those in the middle. To mitigate this positional bias, the authors propose a calibration mechanism called “found-in-the-middle,” which allows the model to attend to contexts based on their relevance. The found-in-the-middle approach not only improves performance in locating relevant information within long contexts but also boosts retrieval-augmented generation (RAG) performance across various tasks by up to 15 percentage points. This research has implications for understanding LLM attention bias and its potential consequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at why large language models struggle to find important information in the middle of what they’re reading. The researchers discovered that these models tend to focus on the beginning and end of what they’re reading, rather than the middle. To solve this problem, they created a new way for the models to pay attention to information based on how important it is. This new approach not only helps models find important information better but also makes them better at generating text that’s relevant to what they’ve read. |
Keywords
* Artificial intelligence * Attention * Rag * Retrieval augmented generation
