In a recent development, the DeepSeek LLM has emerged as a formidable force in the realm of language models, boasting an impressive 67 billion parameters. Trained meticulously from scratch on an expansive dataset of 2 trillion tokens in both English and Chinese, the DeepSeek LLM has set new standards for research collaboration by open-sourcing its 7B/67B Base and 7B/67B Chat versions. This article delves into the model’s exceptional capabilities across various domains and evaluates its performance in intricate assessments.

Superior General Capabilities

DeepSeek LLM 67B Base has proven its mettle by outperforming the Llama2 70B Base in key areas such as reasoning, coding, mathematics, and Chinese comprehension. The model’s prowess extends across diverse fields, marking a significant leap in the evolution of language models.

Proficiency in Coding and Math

A standout feature of DeepSeek LLM 67B Chat is its remarkable performance in coding, achieving a HumanEval Pass@1 score of 73.78. The model also exhibits exceptional mathematical capabilities, with GSM8K zero-shot scoring at 84.1 and Math 0-shot at 32.6. Notably, it showcases an impressive generalization ability, evidenced by an outstanding score of 65 on the challenging Hungarian National High School Exam.

Mastery in Chinese Language

In a head-to-head comparison with GPT-3.5, DeepSeek LLM 67B Chat emerges as the frontrunner in Chinese language proficiency. The evaluation results underscore the model’s dominance, marking a significant stride in natural language processing.

Evaluation Insights

To ensure a fair assessment of DeepSeek LLM 67B Chat, the developers introduced fresh problem sets. This helped mitigate data contamination and catering to specific test sets. The Hungarian National High School Exam serves as a litmus test for mathematical capabilities. And this reveals the model’s prowess in solving complex problems.

Additionally, the “instruction following evaluation dataset” released by Google on November 15th, 2023, provided a comprehensive framework to evaluate DeepSeek LLM 67B Chat’s ability to follow instructions across diverse prompts. The results indicate a high level of competence in adhering to verifiable instructions.

The utilization of LeetCode Weekly Contest problems further substantiates the model’s coding proficiency. By crawling data from LeetCode, the evaluation metric aligns with HumanEval standards, demonstrating the model’s efficacy in solving real-world coding challenges.

Revisiting Multi-Choice Question Benchmarks

An experimental exploration reveals that incorporating multi-choice (MC) questions from Chinese exams significantly enhances benchmark performance. Noteworthy benchmarks such as MMLU, CMMLU, and C-Eval showcase exceptional results, showcasing DeepSeek LLM’s adaptability to diverse evaluation methodologies.

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Our Say

It is evident that DeepSeek LLM is an advanced language model, that stands at the forefront of innovation. Its expansive dataset, meticulous training methodology, and unparalleled performance across coding, mathematics, and language comprehension make it a stand out.

The DeepSeek LLM’s journey is a testament to the relentless pursuit of excellence in language models. As we look ahead, the impact of DeepSeek LLM on research and language understanding will shape the future of AI.