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Tokenization Constraints in LLMs: A Study of Symbolic and Arithmetic Reasoning Limits
Xiang Zhang, Juntai Cao, Jiaqi Wei +2
Tokenization is the first - and often underappreciated - layer of computation in language models. While Chain-of-Thought (CoT) prompting enables transformer models to approximate r…
Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs
Xiang Zhang, Juntai Cao, Jiaqi Wei +2
Despite the remarkable successes of large language models (LLMs), the underlying Transformer architecture has inherent limitations in handling complex reasoning tasks. Chain-of-tho…
Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing
Juntai Cao, Xiang Zhang, Raymond Li +4
Recent advances in test-time scaling have shown promising results in improving Large Language Model (LLM) performance through strategic computation allocation during inference. Whi…
Counting Ability of Large Language Models and Impact of Tokenization
Xiang Zhang, Juntai Cao, Chenyu You
Transformers, the backbone of modern large language models (LLMs), face inherent architectural limitations that impede their reasoning capabilities. Unlike recurrent networks, Tran…
Supervised Chain of Thought
Xiang Zhang, Dujian Ding
Large Language Models (LLMs) have revolutionized natural language processing and hold immense potential for advancing Artificial Intelligence. However, the core architecture of mos…