Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance

TL;DR

This paper compares ChatGPT and DeepSeek across five NLP tasks, revealing their respective strengths and weaknesses in domain-specific performance and reasoning capabilities.

Contribution

It provides a comprehensive, fair evaluation of two prominent LLMs across multiple NLP tasks and domains, highlighting their distinct performance profiles.

Findings

DeepSeek shows superior classification stability and reasoning.

ChatGPT excels in nuanced understanding and flexible tasks.

Both models have domain-specific strengths and weaknesses.

Abstract

The increasing use of large language models (LLMs) in natural language processing (NLP) tasks has sparked significant interest in evaluating their effectiveness across diverse applications. While models like ChatGPT and DeepSeek have shown strong results in many NLP domains, a comprehensive evaluation is needed to understand their strengths, weaknesses, and domain-specific abilities. This is critical as these models are applied to various tasks, from sentiment analysis to more nuanced tasks like textual entailment and translation. This study aims to evaluate ChatGPT and DeepSeek across five key NLP tasks: sentiment analysis, topic classification, text summarization, machine translation, and textual entailment. A structured experimental protocol is used to ensure fairness and minimize variability. Both models are tested with identical, neutral prompts and evaluated on two benchmark datasets per task, covering domains like news, reviews, and formal/informal texts. The results show that DeepSeek excels in classification stability and logical reasoning, while ChatGPT performs better in tasks requiring nuanced understanding and flexibility. These findings provide valuable insights for selecting the appropriate LLM based on task requirements.