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Abstract: “Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i.e., without adaptation on downstream data. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community due to the fact that it can generate high-quality responses to human input and self-correct previous mistakes based on subsequent conversations. However, it is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot. In this work, we empirically analyze the zero-shot learning ability of ChatGPT by evaluating it on 20 popular NLP datasets covering 7 representative task categories. With extensive empirical studies, we demonstrate both the effectiveness and limitations of the current version of ChatGPT. We find that ChatGPT performs well on many tasks favoring reasoning capabilities (e.g., arithmetic reasoning) while it still faces challenges when solving specific tasks such as sequence tagging. We additionally provide in-depth analysis through qualitative case studies.”
Overview of Large Language Model Evaluation on NLP Tasks
The capabilities of LLMs have been expanding, showing remarkable performance in a range of NLP tasks without needing task-specific training data °. These models have sparked discussions about their potential as zero-shot learners ° and generalist models ° that can handle multiple NLP tasks effectively. Among them, ChatGPT has gained particular attention ° due to its ability to produce high-quality responses and correct itself based on conversational cues. Despite these advancements, the question of whether ChatGPT can be deemed a true generalist in solving NLP tasks remains open to investigation.
Evaluation of ChatGPT on NLP Datasets
Researchers conducted an empirical paper to assess the zero-shot learning capabilities ° of ChatGPT by subjecting it to tests across 20 popular NLP datasets spanning seven representative task categories. These included reasoning, natural language inference, question answering, dialogue, summarization, named entity recognition, and sentiment analysis °. The paper compared the performance of ChatGPT with GPT-3.5 ° and other models fine-tuned on task-specific data.
Key Findings
The paper found that ChatGPT outperforms GPT-3.5 in most tasks, particularly ones that require reasoning skills, such as arithmetic reasoning ° and natural language inference. ChatGPT demonstrated superior dialogue handling capabilities and showed effectiveness in sentiment analysis tasks °. However, it encountered challenges with certain domains like sequence tagging, indicating that there remains room for improvement even for advanced models like ChatGPT when it comes to generalizing across all sorts of NLP tasks.
Limitations and Future Directions
While the results highlight ChatGPT's strengths as a zero-shot learner, it was noted that the performance often fell short of models that had been fine-tuned for specific tasks. Additionally, there was evidence that ChatGPT generated responses that were more verbose than necessary in summarization tasks ° and occasionally produced answers not requested in task instructions, such as generating "neutral" in a task requiring "positive" or "negative" sentiments. The paper calls for further exploration of diverse prompting techniques and a closer examination of ChatGPT's few-shot learning capabilities ° compared to its zero-shot performance °.
In summary, ChatGPT has shown potential as a multifaceted tool in the NLP domain ° but still harbors weaknesses that need to be addressed to achieve true generalism across a broader range of language tasks.
- Chengwei Qin ° (24 papers)
- Aston Zhang ° (47 papers)
- Zhuosheng Zhang ° (104 papers)
- Jiaao Chen ° (27 papers)
- Michihiro Yasunaga ° (45 papers)
- Diyi Yang ° (131 papers)