DYVAL: Dynamic Evaluation of Large Language Models for Reasoning Tasks at ICLR 2024

Conceptos Básicos

DYVAL introduces a dynamic evaluation protocol for large language models, addressing data contamination and static complexity in existing benchmarks.

Resumen

1. Introduction

• Large Language Models (LLMs) have shown exceptional performance across tasks.
• Evaluation benchmarks like HELM, Chatbot Arena, AlpacaEval, etc., set standards.
• Challenges include data contamination and fixed complexity levels in benchmarks.

2. DYVAL Framework

• Introduces a dynamic evaluation protocol for LLMs.
• Utilizes directed acyclic graphs (DAGs) to dynamically generate evaluation samples with controllable complexities.
• Evaluates LLMs on reasoning tasks like mathematics, logical reasoning, and algorithms.

3. Data Contamination and Fixed Complexity

• Existing benchmarks face challenges of data contamination from the training corpus and fixed complexity levels.
• Static datasets fail to match the evolving capabilities of LLMs.

4. Results and Analysis

• LLMs perform worse on DYVAL-generated samples with varying complexities.
• Failure cases highlight the need for improvement in LLMs' reasoning abilities.
• Different prompting techniques show varied performances across tasks.

5. Fine-Tuning with DYVAL

• DYVAL-generated data improves the performance of LLMs on existing benchmarks through fine-tuning.
• Larger model sizes tend to achieve better results in complex tasks.

Estadísticas

DYVAL generates challenging evaluation sets on reasoning tasks including mathematics, logical reasoning, and algorithm problems.

Citas

"Large language models have achieved remarkable performance but face challenges of data contamination."
"DYVAL highlights the significance of dynamic evaluation for assessing LLM capabilities."

Ideas clave extraídas de

DyVal

by Kaijie Zhu,J... a las arxiv.org 03-15-2024

https://arxiv.org/pdf/2309.17167.pdf

Consultas más profundas

How can DYVAL's dynamic evaluation approach be applied to other fields beyond natural language processing

DYVAL's dynamic evaluation approach can be applied to various fields beyond natural language processing. For example, in the field of computer vision, DYVAL could dynamically generate image datasets with varying complexities for evaluating the performance of image recognition models. In healthcare, DYVAL could be used to create dynamic evaluation sets for medical diagnosis tasks, adjusting the complexity levels based on the expertise required. Additionally, in finance, DYVAL could generate dynamic scenarios for risk assessment and financial forecasting models.

What counterarguments exist against the use of dynamic evaluation protocols like DYVAL

Counterarguments against the use of dynamic evaluation protocols like DYVAL may include concerns about reproducibility and consistency in evaluations. Critics might argue that dynamically generated datasets could introduce biases or inconsistencies that make it challenging to compare results across different evaluations or studies. There may also be skepticism about the reliability and validity of dynamically generated samples compared to static benchmarks with fixed datasets.

How can the concept of directed acyclic graphs in DYVAL be related to real-world problem-solving scenarios

The concept of directed acyclic graphs (DAGs) in DYVAL can be related to real-world problem-solving scenarios by mimicking complex decision-making processes where multiple interconnected factors influence outcomes without forming cycles or loops. For instance, DAGs can model supply chain networks where products flow from suppliers to manufacturers to retailers without any circular dependencies. In project management, DAGs can represent task dependencies where certain tasks must be completed before others can begin but no circular dependencies exist. By leveraging DAGs in problem-solving scenarios, organizations can better understand intricate relationships and optimize decision-making processes efficiently.