Summary of Focused Large Language Models Are Stable Many-shot Learners, by Peiwen Yuan et al.
Focused Large Language Models are Stable Many-Shot Learners
by Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Heda Wang, Yao Hu, Kan Li
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper investigates the limitations of In-Context Learning (ICL) in large language models (LLMs) when adapting to new tasks through demonstrations. Despite advancements in LLMs, recent experiments have shown that the performance of ICL does not scale linearly with the increase in demonstration context length. The authors attribute this to more demonstrations spreading attention away from the query, making it harder for the model to grasp key content. To address this issue, they propose FocusICL, a training-free method that filters out trivial information at the token level and employs hierarchical attention to maintain focus on the current query at the demonstration level. The authors also develop an efficient hyperparameter search strategy based on model perplexity. Experimental results demonstrate that FocusICL achieves an average performance improvement of 5.2% over vanilla ICL and is more effective in many-shot demonstration settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks into why large language models have trouble learning from examples, even when they have a lot to learn from. They found that when there are many examples, the model gets distracted and can’t focus on what’s important. To fix this problem, the authors developed a new method called FocusICL. This method helps the model ignore unimportant information and pay attention to what it needs to learn. The authors also came up with a way to find the best settings for their method without having to train the model again. By using these methods, they were able to make the model perform 5.2% better than before. |
Keywords
» Artificial intelligence » Attention » Context length » Hyperparameter » Perplexity » Token
