Core Concepts
LLMs rely on semantic shortcuts rather than proper reasoning, leading to hallucinations and failures in complex QA tasks.
Abstract
The content discusses the phenomenon of semantic shortcuts in large language models (LLMs) and their impact on reasoning capabilities. It introduces a novel probing method and benchmark called EUREQA to evaluate LLMs' ability to follow correct reasoning paths. The experiments show that existing LLMs struggle with deceptive semantic associations, relying more on biases than proper reasoning. The study questions the validity of current high-performance language models.
Structure:
Introduction:
Recent advancements in LLMs across various reasoning tasks.
Investigating if LLMs rely on sensible reasoning paths or semantic associations.
EUREQA Dataset:
Constructing extended reasoning chains for evaluation.
Filtering viable reasoning chains based on knowledge base queries.
Experiment Setup:
Configurations for evaluating ChatGPT and GPT-4 on EUREQA.
Results:
Performance analysis of LLMs across different depths of reasoning and levels of difficulty.
Analysis and Discussions:
Observations regarding entity similarities, human analysis, open-source model performance, prompting techniques, and RAG method study.
Related Work:
Discussion on hallucination in LLMs and their reasoning capabilities.
Conclusion:
Summary of the study's findings and ethical considerations.
Stats
存在するLLMの性能は、EUREQAで62%の精度しか達成していない。
GPT-4はWikipediaとChatGPTで40%未満の精度を達成している。
人間はほぼ完璧なパフォーマンスを達成している。
Quotes
"Experiments show that existing LLMs cannot follow correct reasoning paths."
"Our analysis provides further evidence that LLMs rely on semantic biases to solve tasks."