NLPBench: Evaluating Large Language Models on Solving NLP Problems

NLPBench: Evaluating Large Language Models on Solving NLP Problems
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Original Paper
Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a dearth of research dedicated to the NLP problem-solving abilities of LLMs. To fill the gap in this area, we present a unique benchmarking dataset, NLPBench, comprising 378 college-level NLP questions spanning various NLP topics sourced from Yale University's prior final exams. NLPBench includes questions with context, in which multiple sub-questions share the same public information, and diverse question types, including multiple choice, short answer, and math. Our evaluation, centered on LLMs such as GPT-3.5/4, PaLM-2, and LLAMA-2, incorporates advanced prompting strategies like the chain-of-thought (CoT) and tree-of-thought (ToT). Our study reveals that the effectiveness of the advanced prompting strategies can be inconsistent, occasionally damaging LLM performance, especially in smaller models like the LLAMA-2 (13b). Furthermore, our manual assessment illuminated specific shortcomings in LLMs' scientific problem-solving skills, with weaknesses in logical decomposition and reasoning notably affecting results.

Summary Notes

Evaluating LLMs in NLP: Insights from NLPBench

The rapid advancements in Large Language Models (LLMs) have significantly impacted the field of artificial intelligence, particularly in understanding and generating human language.
For AI Engineers in corporate environments, grasping the strengths and weaknesses of these models in tackling complex Natural Language Processing (NLP) tasks is vital. This blog provides insights from a study that leveraged NLPBench, a benchmark for evaluating LLMs on NLP-related problems, offering a deep dive into their problem-solving abilities.

NLPBench Dataset

Central to the study is the NLPBench dataset, which consists of:
  • A variety of questions from NLP course finals, including multiple-choice, short answers, and math problems.
  • Tests on both NLP knowledge and logical reasoning skills.
  • Categories for context-dependent and independent questions, assessing models' information processing abilities in different scenarios.

Study Methodology

The study assessed leading LLMs such as GPT-3.5/4, PaLM-2, and LLAMA-2 (13b and 70b versions) through:
  • Advanced prompting strategies like chain-of-thought (CoT) and tree-of-thought (ToT).
  • Traditional few-shot and zero-shot prompting techniques.
  • Evaluation metrics focused on accuracy, error analysis, and prompting techniques' effectiveness.

Key Findings

The study highlighted several important patterns:
  • GPT-4's Superiority: GPT-4 consistently outperformed other models, demonstrating its advanced design and training.
  • Variable Few-shot Prompting Benefits: Few-shot prompting sometimes improved performance, but not uniformly across all tasks and models.
  • Advanced Prompting Strategy Efficacy: CoT and ToT prompting showed variable success, especially in smaller models, indicating these strategies need more refinement.
  • Logical Reasoning Challenges: All models struggled with tasks requiring logical problem-solving, pointing to an area for future improvement.

Text Relevance Evaluation

The study also explored text relevance metrics, finding that high relevance scores (like those of PaLM-2) did not always correlate with high accuracy in solving NLP problems. This suggests that text relevance alone is an incomplete measure of a model's performance on complex NLP tasks.


The NLPBench dataset's complexity unveiled significant challenges for LLMs, particularly in logical reasoning and understanding intricate NLP concepts.
While basic prompting methods were effective, the varying success of advanced strategies indicates a need for ongoing refinement. The study underscores the necessity of boosting LLMs' logical thinking abilities to enhance their problem-solving skills.


NLPBench provides a robust framework for assessing LLMs' abilities to tackle NLP problems. The study reveals both the impressive capabilities and the reasoning gaps of current models.
For AI Engineers, these insights highlight the criticality of carefully selecting and tuning LLMs for specific needs and the importance of improving models' logical reasoning skills.
Future efforts should aim at refining prompting strategies and bolstering LLMs' logical deduction capabilities, advancing the development of more sophisticated AI systems.


A special thanks to Professor Dragomir Radev and the students who developed the NLPBench dataset. Their work is crucial for furthering our understanding of LLMs and inspiring future innovations in NLP.
In summary, ongoing studies like this are essential for pinpointing the strengths and limitations of LLMs.
By directly addressing these challenges, we can unlock LLMs' full potential in solving complex NLP tasks, pushing the boundaries of AI's ability to comprehend and interact with human language.

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