Large language models are good at many tasks but bad at complex reasoning, especially when it comes to math problems. Current In-Context Learning (ICL) methods depend heavily on carefully chosen examples and human help, which makes it hard to handle new problems. Traditional methods also use straightforward reasoning techniques that limit their ability to look for different solutions, making them slow and not great for various situations. It is again important to confront these challenges to enhance automated reasoning, adaptability, and proper use of LLMs.
Traditional ICL techniques, such as Chain-of-Thought (CoT) reasoning and zero/few-shot prompting, have shown promise in enhancing reasoning performance. CoT enables models to think about problems step by step, which is great for solving structured issues. However, these methods have big problems. Their performance depends on how good the examples are and how they are structured, which requires a lot of skill to prepare. The models cannot adapt to problems that deviate from their training examples, reducing the utility in diverse tasks. Moreover, current approaches rely on sequential reasoning, which restricts the exploration of alternative problem-solving strategies. These limitations have indicated a need for innovative frameworks that reduce human dependency, enhance generalization, and optimize reasoning efficiency.
HiAR-ICL (High-level Automated Reasoning in In-Context Learning) addresses these challenges by reimagining “context” as encompassing higher-order reasoning patterns instead of focusing on example-based learning. This paradigm fosters adaptability and robustness in problem-solving by cultivating transferable reasoning capabilities. It aggregates five salient thought processes: System Analysis (SA), One-Step Thought (OST), Chain-of-Thought (CoT), Divide-and-Conquer (DC), and Self-Reflection and Refinement (SRR), for it to function like human solving processes. These are the basis on which “thought cards,” reusable reasoning templates, come to be constructed using the Monte Carlo Tree Search(MCTS) mechanism. MCTS identifies optimally good reasoning paths from a seed dataset, which then are distilled into abstract templates. A cognitive complexity framework evaluates problems along dimensions that include subquestion count, condition complexity, and semantic similarity, which dynamically informs the selection of relevant and precise thought cards. This dynamic reasoning process is further enhanced by multi-layered validation techniques, including self-consistency and reward-based evaluations, ensuring accuracy and reliability.
HiAR-ICL demonstrates significant advancements in reasoning accuracy and efficiency across various benchmarks. Its performance is best on datasets like MATH, GSM8K, and StrategyQA. Accuracy increases by as much as 27% compared to traditional ICL methods. Efficiency is also impressive with computing time cut down by as much as 27 times for easier tasks and up to 10 times for harder problems. It does well with varied applications and even small models; thus, accuracy improves in many tests by more than 10%. Its capability of surpassing traditional approaches while accommodating a range of difficult problems promises the revolution of this discipline.
HiAR-ICL redefines reasoning capabilities in LLMs by transitioning from example-centric paradigms to high-level cognitive frameworks. Monte Carlo Tree Search and the use of thought cards for problem-solving make it a robust tool to work adaptively with very minimal need for human help. It was able to come up at the top when its performance was tested with hard tests, indicating its strength in shaping the future of automated reasoning, especially through efficient handling of complex tasks.
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