Mamba and Other Reasoning Shortcomings: Are Original Transformers Still the Best?
By [Your Name], Machine Intelligence
AIxiv Exclusive
The emergence of chain-of-thought (CoT) reasoning has been a game-changer in the realm of large language models (LLMs), particularly inenhancing their performance on mathematical tasks. However, the introduction of CoT has also brought about a significant increase in the length of generated content, leading to higher computationalcosts. This begs the question: are the newly introduced, efficient models like Mamba truly a step forward, or are original Transformers still the best?
Two independent research teams from Peking University and Tsinghua University have delved into this intriguingquestion, shedding light on the hidden limitations of CoT reasoning. Their findings, published at ICML 2024, suggest that while CoT methods have undeniably improved the capabilities of Transformers, they come with their own set of challenges.
The Peking University team, led by Professors Liwei Wang and Di He from the School of Intelligence, conducted a comprehensive analysis of the performance of various LLMs, including those equipped with CoT techniques. Their research, involving undergraduate student Kai Yang, master’s student Jan Ackermann, and doctoral students Zhenyu He,Guhao Feng, Bohang Zhang, Yunzhen Feng, and Qiwei Ye, revealed that while CoT models excel in certain tasks, they often struggle with more complex reasoning problems.
Simultaneously, a research group from Tsinghua University, led by Kai Feng Lü, a Simons Institute post-doctoral fellow and soon-to-be assistant professor at Tsinghua University’s Cross-Disciplinary Information Institute, conducted a similar investigation. Their team, including doctoral student Kaiyue Wen and undergraduate student Xingyu Dang, found that the efficiency gains achieved by models like Mamba often come at the cost of accuracy, especially in scenarios requiring intricate reasoning.
These findings highlight the trade-offs inherent in the pursuit of efficiency and accuracy in LLMs. While CoT methods have undoubtedly pushed the boundaries of reasoning capabilities, they are not a panacea. The research suggests that the original Transformer architecture, despite its computational demands, may still hold the key to unlocking the full potential ofLLM reasoning.
The research from both Peking University and Tsinghua University underscores the need for continued exploration and development of LLM architectures. The future of AI lies in finding the optimal balance between efficiency and accuracy, ensuring that these powerful tools can effectively tackle the most challenging problems.
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