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
LongICLBench, a new benchmark, evaluates 13 LLMs on long in-context learning, revealing a performance decline in complex tasks & urging the development of models with deeper semantic understanding: https://t.co/KeM9BX01o4 https://t.co/A5pSSr7dUR
Long-context LLMs Struggle with Long In-context Learning! 🤯 We developed LongICLBench to rigorously test LLMs on extreme classification tasks with increasing complexity. We meticulously selected six datasets with a label range spanning 28 to 174 classes covering different input… https://t.co/nYzL6fwyAb https://t.co/xMNBdyXCJ2
Long-context LLMs Struggle with Long In-context Learning Large Language Models (LLMs) have made significant strides in handling long sequences exceeding 32K tokens. However, their performance evaluation has largely been confined to metrics like perplexity and synthetic tasks, https://t.co/5lYSqk34CA
Long-context LLMs Struggle with Long In-context Learning Suggests a notable gap in current LLM capabilities for processing and understanding long, context-rich sequences. https://t.co/RAYvvpry50 https://t.co/pkvRhoo9fY
Are advanced LLMs edging out even the need for category fine-tuning? The Power Era in AI and LLMs 🔴Generically trained LLMs like GPT-4 are surpassing fine-tuned models in specialized domains. 🔴Challenges in fine-tuning, such as dataset specificity and high costs, are…
ST-LLM Large Language Models Are Effective Temporal Learners Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation, prompting research efforts towards video LLMs to facilitate human-AI interaction at the video level. However, https://t.co/ETvv4NX312
Quick Start Guide to Large Language Models — Strategies and Best Practices for Using #LLMs: https://t.co/UEgcGVEkVv ————— #BigData #DataScience #AI #NLProc #NeuralNetworks #DeepLearning #MachineLearning #Algorithms https://t.co/E9Tp671is7
Research on LLMs is moving quickly, and even models / techniques that have been state-of-the-art for a long time (e.g., GPT-4 and Mixtral) are being quickly dethroned. Here’s a list of my top ten AI developments (each with a brief summary) over the last few months… [1] DBRX is… https://t.co/05A4Fy7dCG
Understanding Small Language Models: How they stack up against LLMs #LLM https://t.co/FTWGM5qFb7
🚀Transform your data strategies with our upcoming Large Language Models Bootcamp! 📌Save your seat today: https://t.co/IDPIH6Mo5L #bootcamp #LargeLanguageModel #AI #Azure #LangChain #LLM #artificialintelligence https://t.co/7wdKX3rW6b
Last month Microsoft research introduced LongRoPE, for extending the context window of pre-trained LLMs to a massive 2mn tokens while preserving performance at the original short context window. 📌 Without the need of direct fine-tuning on texts with extremely long lengths 📌… https://t.co/3z7pEW81IF
⚡️The Power Era in AI and LLMs 👉Are advanced LLMs edging out even the need for category fine-tuning? Data and model training are at the heart of the competition with large language models (LLMs). But a compelling narrative is unfolding, one that could very well redefine our… https://t.co/i3tU6YoecD
🚨Does "fine-tuning" matter anymore as LLM models advance rapidly? 👉👉👉It's seems that Bloomberg's private GPT3 trained LLM can't beat the generic GPT4 platform. Training data and human reinforcement might be futile in the context of new generation LLMs and the specter of…