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LLMs Struggle with Complex Problem Solving Despite Recent Advances

Large Language Models (LLMs) like GPT-4 have shown impressive abilities in various tasks. However, a new survey paper reveals that applying them to complex, real-world problems is still a challenge. The paper, titled “Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey,” identifies three key components of complex problem-solving where LLMs currently fall short: multi-step reasoning, domain knowledge integration, and result verification.

Unlike simple tasks, complex problems require a series of interconnected steps to arrive at a solution. This “multi-step reasoning” is difficult for LLMs because the number of possible solution paths grows exponentially with each step, making it hard to find the correct one. Any error along the way can propagate, leading to a wrong final answer. For example, imagine an LLM trying to design a complex software system. It needs to not only write correct code, but also consider the scalability, reliability, and user needs - a series of interconnected design decisions.

Another challenge is integrating “domain knowledge.” Real-world problems often require specialized knowledge that LLMs may not possess or struggle to recall. This is particularly true for “long-tail” knowledge, information that is less common and therefore less represented in the LLM’s training data. Data science provides a good example: building accurate models requires a strong understanding of both the data and a wide range of modeling techniques.

Finally, “result verification” is crucial. It involves assessing whether each step in the problem-solving process contributes to a correct solution, but it can be particularly difficult when there are no standard outcomes or predefined procedures. Imagine an LLM tasked with data mining to discover insightful patterns. What constitutes an “insightful” pattern can be subjective and context-dependent, making verification challenging.

To address these limitations, researchers are exploring several techniques. One promising approach is “Chain-of-Thought” (CoT) reasoning, where the LLM is prompted to explicitly show its reasoning steps. Another is “knowledge augmentation,” where external knowledge bases are integrated to provide the LLM with the necessary domain expertise. Additionally, researchers are developing various methods for result verification, including LLM-as-a-judge approaches, symbolic reasoning tools, and experimental validation systems.

The survey paper also highlights the unique challenges in applying LLMs to specific domains, including software engineering, mathematics, data science, and scientific research. It argues that future research should focus on improving LLMs’ multi-step reasoning abilities, knowledge integration capabilities, and result verification techniques. This includes building multi-agent collaboration frameworks, combining LLMs with external tools and improving the feedback loop between LLM-generated outputs and human validation. By overcoming these challenges, LLMs can evolve from basic assistants into powerful tools for solving complex, real-world problems.