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The advancement of Large Language Models (LLMs) like GPT-4 is challenging the traditional approach of fine-tuning models for specific tasks. Research indicates that generic LLMs are surpassing fine-tuned models in specialized domains, raising questions about the necessity and effectiveness of fine-tuning. New benchmarks like LongICLBench are being developed to evaluate LLMs on long in-context learning, highlighting performance declines in complex tasks and the need for models with deeper semantic understanding.
[CL] Long-context LLMs Struggle with Long In-context Learning T Li, G Zhang, Q D Do, X Yue, W Chen [University of Waterloo] (2024) https://t.co/xcXqYJDKpF - The paper proposes LongICLBench, a benchmark for evaluating long in-context learning on extreme-label text classification… https://t.co/CDK1IOyh92
Long Context LLMs Struggle with Long In-Context Learning Finds that after evaluating 13 long-context LLMs on long in-context learning the LLMs perform relatively well under the token length of 20K. However, after the context window exceeds 20K, most LLMs except GPT-4 will dip… https://t.co/BmvxUQY1i2
How well can LLMs reason? "Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions"… https://t.co/gz06EJxqPI
