Working Memory Identifies Reasoning Limits in Language Models (2024)

Abstract: This study explores the inherent limitations of large language models (LLMs) from a scaling perspective, focusing on the upper bounds of their cognitive capabilities. We integrate insights from cognitive science to quantitatively examine how LLMs perform on n-back tasks—a benchmark used to assess working memory, which involves temporarily holding and manipulating information. Our findings reveal that despite the increased model size, LLMs still face significant challenges in holding and processing information effectively, especially under complex task conditions. We also assess various prompting strategies, revealing their diverse impacts on LLM performance. The results highlight the struggle of current LLMs to autonomously discover optimal problem-solving patterns without heavily relying on manually corrected prompts. To move beyond these constraints, fundamental improvements in the planning and search of LLMs are essential for them to reason autonomously. Improving these capabilities will reduce the reliance on external corrections and enable LLMs to become more autonomous in their problem-solving processes.

## introduction working memory is integral to human reasoning, serving as the cognitive system that temporarily holds and manipulates information for complex tasks like learning, reasoning, and comprehension @xcite . understanding working memory is pivotal not only for grasping human cognitive processes but also for exploring the capabilities and limitations of llms. cognitive limits represent the thresholds where the efficiency of information processing and retention starts to wane, critical for pushing the boundaries of what llms like instructgpt and chatgpt can achieve @xcite @xcite . as models like instructgpt and chatgpt evolve (see tab.1), their enhanced capabilities necessitate a comparison to human cognitive processes, especially regarding working memory and cognitive limits @xcite . llms have excelled in various natural language tasks, often surpassing domain-specific models in areas such as machine translation and visual qa @xcite @xcite . this success is attributed to their advanced reasoning abilities, honed through innovative prompting methodologies such as the chain of thought (cot), self-consistency, and automated reasoning @xcite @xcite . in this study, we explore the boundaries of llms' working memory, focusing on their performance in handling information under various prompting conditions. our analysis reveals that despite their advanced capabilities, llms struggle with specific complex tasks from the big-bench hard (bbh) dataset, highlighting a stark contrast to human problem-solving abilities @xcite . this discrepancy challenges the prevailing notion of llms' autonomy and self-corrective reasoning @xcite @xcite . our comparative analysis reveals significant reasoning limitations in scaled models, especially when compared to the general intelligence of humans. these limitations manifest primarily in an autonomy deficit within prompts, rendering them ineffective for enabling llms to autonomously uncover problem-solving patterns, and a lack of prompt effectiveness in enhancing comprehension of world knowledge and logic, key components for reasoning through answers effectively. ## cognitive limits of llms as reasoners tasks to assess the reasoning capabilities of llms, we utilize the bbh set containing 23 tasks, which are categorized into two main dimensions of reasoning: world knowledge and logic. this categorization is visually represented in fig. 2 . ## working memory's pivotal role as llms struggle to recognize essential pattern of problem solving, their limitations invite an indepth discussion from the perspective of working memory-a crucial intrinsic ability that entails temporary storage and manipulation of information for reasoning within cognitive science (see fig. 4 ). this capacity is central not only to human cognition but also to the functioning of artificial intelligence systems. the comparison between llm and human cognitive processes reveals gaps and offers heuristic strategies to mitigate the current limitations of llm. this insight underpins the call for further developmental strides aimed fundamentally at enhancing problem-solving capabilities in llms. using our understanding of working memory, we can devise interventions that improve the efficiency and accuracy of llms in tasks that require complex cognitive operations. ## applying cognitive strategies to mitigate reasoning limits in llms to address reasoning challenges in llms due to their limited working memory capacity, we incorporate cognitive science principles aimed at enhancing their ability to effectively utilize working memory. this approach improves llms in recalling world knowledge and executing logical manipulations. these strategies and open-source reasoning models are detailed at app.h & g. the development of these strategies is currently manual, prompting an important question for future research: how can we develop foundation models with greater intrinsic autonomy to automatically overcome these reasoning limitations? ## conclusion and future work we explore the working memory capacities and reasoning limitations of llms within the context of complex cognitive tasks. despite advancements in model scaling and the development of sophisticated prompting techniques such as cot+ prompts, llms continue to exhibit significant discrepancies in performance compared to human cognitive capabilities. our findings underscore the fundamental limitations of llms in autonomously recognizing and applying complex problem-solving patterns without direct human intervention. the intrinsic capacity constraints of llms, particularly in terms of working memory, pose a critical challenge. these limitations are not merely technical but are also a reflection of the current state of ai development, which still heavily relies on human-engineered solutions. our mitigation strategies, while effective to some extent, primarily enhance performance in well-defined, controlled scenarios and do not universally translate to the diverse, unstructured challenges present in real-world applications. as we move forward, it is imperative to focus on developing foundation models that can independently plan and search through complex logical constructs and world knowledge. enhancing the working memory capacities of llms and reducing their reliance on human-crafted prompts are essential steps toward achieving this goal. such advancements will not only bridge the gap between artificial and human intelligence but also expand the autonomy of llms in complex, dynamic, realworld environments.