Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge (2310.05146v1)

Abstract: We attempt to solve the Abstraction and Reasoning Corpus (ARC) Challenge using LLMs as a system of multiple expert agents. Using the flexibility of LLMs to be prompted to do various novel tasks using zero-shot, few-shot, context-grounded prompting, we explore the feasibility of using LLMs to solve the ARC Challenge. We firstly convert the input image into multiple suitable text-based abstraction spaces. We then utilise the associative power of LLMs to derive the input-output relationship and map this to actions in the form of a working program, similar to Voyager / Ghost in the MineCraft. In addition, we use iterative environmental feedback in order to guide LLMs to solve the task. Our proposed approach achieves 50 solves out of 111 training set problems (45%) with just three abstraction spaces - grid, object and pixel - and we believe that with more abstraction spaces and learnable actions, we will be able to solve more.

Analysis of "LLM as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge"

The paper "LLM as a System of Multiple Expert Agents" by John Tan Chong Min and Mehul Motani proposes a novel approach to tackle the Abstraction and Reasoning Corpus (ARC) challenge using Multiple Expert Agents facilitated by LLMs. The ARC challenge, a benchmark intended to test the capabilities of AI systems in forming abstractions and reasoning from very few examples, has been considered a step towards artificial general intelligence (AGI). Unlike mainstream deep learning models that perform well on large datasets, the ARC challenge emphasizes the necessity of learning with minimal samples, highlighting the inherent limitations in current AI paradigms.

Methodology and Approach

The authors address this challenge by employing LLMs, specifically GPT-4, as a system of multiple expert agents without pre-training to solve ARC tasks. The primary innovation in their method lies in converting the input spaces into various abstraction spaces, specifically grid, object, and pixel spaces, thus enabling a mapping between input-output associations through program synthesis. Their approach represents an intermediary path between deep learning’s flexibility and the rapid adaptability of traditional symbol-based systems, akin to Good Old-Fashioned AI (GOFAI).

1. Abstraction Spaces: Min and Motani utilize distinct abstraction spaces—grid, object, and pixel—to reformulate the input problem, reflecting on different aspects of the ARC tasks. These spaces serve as multiple expert agents, each with its perspective on the transformation problem. This induces a functional operational space more accommodating than conventional symbolic representations used in GOFAI.
2. Grounding in Functional Space: The paper discusses how the associative power of LLMs in grounding different abstraction spaces can facilitate program synthesis. By utilizing crafted primitive functions grounded through language, they streamline the mapping process from input to output, thereby enabling effective program creation for the synthesized solutions.
3. Iterative Environmental Feedback: To enhance the LLMs' adaptability, the authors incorporate an iterative environmental feedback loop, allowing agent refinement through recursive problem attempts. This resembles methodologies utilized in AI systems like Voyager and Ghost in MineCraft, where iterative learning from environmental interaction is crucial to improving the output.

The paper reports a 45% solve rate on a subset of ARC tasks when employing their method, using only three abstraction spaces. This demonstrates an effective application of LLMs in task-specific problem-solving via systematic grounding and abstraction, though the solve rate still leaves room for further enhancement.

Implications and Future Directions

The paper’s findings suggest several implications for both theoretical AI development and practical applications:

• AI System Design: The approach advocates for a hybrid AI model design leveraging the adaptability and broad contextual understanding of LLMs while encoding task-specific symbol-like grounding through expert agents, which could potentially widen AI applications where traditional deep learning models fall short.
• Further Abstraction Integration: As inferred from their methodology, integrating and examining additional abstraction spaces or combinations, such as symmetry or difference views, might extend the solve rate and robustness of the solution. Future work might focus on automating the learning and implementation of novel abstraction insights based on task-specific requirements.
• Enhanced Memory and Contextual Utilization: Since empowering LLMs with a memory bank and enhanced context handling remains underutilized due to current technical constraints, advancements in these areas could significantly escalate performance ratings by enabling more nuanced learning from past task examples.

Overall, Min and Motani’s work enriches the discourse on bridging gaps between deep learning and symbolic reasoning using LLMs, showcasing significant potential in complex problem-solving through structured abstraction and iterative learning frameworks.