The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs
Overview
Paper Summary
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.
Explain Like I'm Five
Imagine asking a computer to add numbers many times in a row. It might get the first few right but starts making mistakes as it goes, like forgetting what it was doing.
Possible Conflicts of Interest
None identified
Identified Limitations
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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