DetectBench: A Benchmark for Evaluating Large Language Models' Detective Skills

Conceitos essenciais

Existing models perform poorly in information detection and reasoning, but the Detective Thinking Framework improves performance.

Resumo

Detectives engage in information detection and reasoning simultaneously.
DetectBench assesses models' abilities in key information detection and multi-hop reasoning.
Proposed Detective Thinking Framework enhances model's detective skills.
Experiments show humans outperform LLMs, emphasizing the importance of detecting clues before reasoning.
Detective Thinking Prompt and Finetune methods significantly enhance model's performance.

Estatísticas

DetectBench comprises 3,928 questions paired with paragraphs averaging 190 tokens.
Experiments reveal existing models perform poorly in both information detection and multi-hop reasoning.
Humans significantly outperform LLMs in clue detection and answering questions.

Citações

Principais Insights Extraídos De

Piecing Together Clues

by Zhouhong Gu,... às arxiv.org 03-21-2024

https://arxiv.org/pdf/2307.05113.pdf

Perguntas Mais Profundas

How can the Detective Thinking Framework be applied to other language processing tasks?

Detective Thinking Framework can be applied to other language processing tasks by guiding models to consider all possible clues comprehensively before reasoning. This approach encourages a systematic process of detail detection, detail association, answer inspiration, and weighted reasoning. By incorporating this framework into various tasks, models can improve their ability to detect key information and engage in multi-step reasoning effectively.

What are potential drawbacks of heavily relying on large language models for detective work?

One potential drawback of heavily relying on large language models for detective work is the risk of overlooking subtle or implicit clues that may not be explicitly stated in the text. These models may struggle with detecting nuanced details or making connections between disparate pieces of information, leading to inaccurate conclusions. Additionally, large language models may lack the contextual understanding and common sense reasoning abilities that human detectives possess, limiting their effectiveness in complex investigative scenarios.

How can the concept of "detective thinking" be integrated into everyday problem-solving scenarios?

The concept of "detective thinking" can be integrated into everyday problem-solving scenarios by encouraging individuals to adopt a systematic approach similar to that used by detectives. This involves carefully examining all available information, identifying key details or clues relevant to the problem at hand, connecting these clues through logical reasoning processes, and arriving at informed conclusions. By training individuals to think like detectives in their problem-solving endeavors, they can enhance their analytical skills and decision-making capabilities across various situations.