Summary of Distance Between Relevant Information Pieces Causes Bias in Long-context Llms, by Runchu Tian et al.
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs
by Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitation of large language models (LLMs) in processing long inputs, specifically the “lost in the middle” phenomenon where they struggle to utilize relevant information situated in the middle of the input. To tackle this issue, the authors introduce LongPiBench, a benchmark designed to evaluate positional bias involving multiple pieces of relevant information. The study employs five commercial and six open-source models, revealing that while most current models are robust against the “lost in the middle” issue, significant biases remain related to the spacing of relevant information pieces. These findings underscore the importance of evaluating and reducing positional biases to enhance LLM’s capabilities. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making big language models better at understanding long texts. Right now, these models can get stuck when they encounter important information in the middle of what they’re reading. To solve this problem, researchers created a special test called LongPiBench that looks at how well different models handle multiple pieces of important information. They tried five popular models and six other ones from online. The results show that while most models are good at handling the “middle” issue, there’s still a big problem with how far apart these important bits are. This is important because it means we need to find ways to make language models better at understanding long texts. |