LLM "Reasoning" is a Mirage? It Struggles with New Problems!

Descripción general

Resumen del artículo › Explícamelo como si tuviera cinco años › Conflictos de intereses › Limitaciones identificadas › Explicación de la calificación › Conviene saber › Jerarquía temática › Información del archivo ›

Resumen del artículo

Título de Paperzilla

This study investigated Chain-of-Thought reasoning in LLMs using a controlled environment, revealing its limitations in handling novel tasks, lengths, and formats. This implies that the apparent "reasoning" may be due to memorization rather than logical inference, emphasizing the need for more robust reasoning models.

Explícamelo como si tuviera cinco años

If you teach a computer to solve problems step-by-step using only specific examples, it might struggle with new problems that look different or require different steps.

Posibles conflictos de intereses

None identified.

Limitaciones identificadas

Synthetic dataset

The training and testing process is conducted on a synthetic dataset that may not entirely reflect real-world scenarios.

Potential for coincidental results

The model's performance on certain aspects of reasoning, such as element and length generalization, may be influenced by factors like random coincidences in generated output.

Choice of LLM

The reliance on GPT-2 as the language model may limit the generalizability of findings to larger, more powerful LLMs.

Simplified reasoning

The simplification of the reasoning process and the definition of 'generalization' using simple transformations on atomic elements may not capture the full complexity of reasoning seen in more complex real-world scenarios.

Focus on decoder-only LLMs

The study primarily focuses on decoder-only LLMs, potentially overlooking specific characteristics of encoder-decoder models.

Explicación de la calificación

This paper offers valuable insights into the limitations of chain-of-thought prompting and introduces a novel framework for systematic analysis. The controlled environment and the focus on distributional shifts provide a solid foundation for understanding the generalization capabilities of LLMs.

Conviene saber

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Jerarquía temática

Dominio: Ciencias físicas

Campo: Ciencias de la computación

Subcampo: Inteligencia artificial

Información del archivo

Título original: Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens

Subido: 11 ago 2025, 16:53:38

Privacidad: Público