The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs

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Overview

Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

Paperzilla title

LLMs Can Barely Tie Their Shoes: Even Simple Tasks Become Impossible When Made Longer

This study explores the ability of Large Language Models (LLMs) to perform long-horizon tasks, finding that even simple, repetitive tasks become extremely challenging when extended over many steps. While LLMs often excel at single steps, their performance degrades rapidly as the task length increases, primarily due to a "self-conditioning" effect where past mistakes increase the likelihood of future errors.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Limited generalizability

Findings based on specific pre-trained LLMs and might change with fine-tuning or different model architectures.

Synthetic task

The research relies on a simplified, synthetic task of adding numbers in a key-value dictionary, which may not fully represent the complexity of real-world tasks.

Lack of real-world application

While the findings offer interesting insights into LLM behavior, the practical implications for real-world tasks remain to be explored.

Focus on a narrow capability

The study focuses specifically on execution ability, isolating it from other aspects of LLM performance like planning and knowledge retrieval, which are also crucial for real-world tasks.

Rating Explanation

This paper presents a novel and insightful analysis of a crucial aspect of LLM performance. The methodology of isolating execution capability is well-designed, and the findings are interesting and potentially significant. While the limitations related to the synthetic nature of the task and limited generalizability are acknowledged, the study makes a valuable contribution to understanding LLM behavior. It could inspire future research on mitigating the identified weaknesses and scaling LLMs for more complex, real-world tasks.

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