Source-linked AI summary
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, Diyi Yang
TL;DR
The paper asks whether ChatGPT can generalize as a zero-shot solver across many NLP tasks. It evaluates ChatGPT against GPT-3.5 and other reported systems across diverse datasets, finding strong performance on reasoning and dialogue but weaknesses on sequence tagging and other specific tasks.
Problem
It remains unclear whether ChatGPT achieves better zero-shot generalization than existing LLMs across diverse NLP tasks.
Method
The study empirically compares ChatGPT and GPT-3.5 across diverse NLP tasks using zero-shot prompting, including zero-shot chain-of-thought for reasoning tasks.
Results
ChatGPT is strong on reasoning and dialogue tasks but still faces challenges on specific tasks such as sequence tagging, and often trails task-fine-tuned models.
Takeaways & Limitations
The results provide a profile of ChatGPT’s zero-shot capabilities, showing effectiveness alongside task-dependent limitations.
Takeaways & Limitations
ChatGPT’s cost limits the study’s larger-scale datasets and task coverage, and its few-shot in-context learning remains un compared with zero-shot learning.
Abstract
from arXiv · showhide
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.
1 Introduction
The paper asks whether ChatGPT generalizes zero-shot across diverse NLP tasks and empirically profiles its strengths and limitations. It finds stronger performance on several reasoning-oriented tasks but persistent weaknesses on specialized tasks.
- Research questions: The study examines whether ChatGPT is a general-purpose NLP task solver and identifies the task types where it performs well.The evaluation targets zero-shot generalization across representative NLP categories.
- Study design: The authors compare ChatGPT with GPT-3.5 and report results from fine-tuned or few-shot systems such as FLAN, T0, and PaLM.The comparison spans zero-shot, fine-tuned, and few-shot fine-tuned results from prior work.
- Key findings: ChatGPT shows generalist capability but often performs worse than models fine-tuned on task-specific data.This establishes effectiveness alongside a limitation of zero-shot generalization.
- Key findings: ChatGPT is strong on arithmetic reasoning, natural language inference, reading comprehension, dialogue, and sentiment analysis, but results vary across reasoning types.It can underperform GPT-3.5 on commonsense, symbolic, and logical reasoning despite stronger arithmetic reasoning.
- Key findings: Both ChatGPT and GPT-3.5 face challenges on sequence tagging, while limiting summary length further harms summarization quality.The findings show task-specific weaknesses alongside broader generalist promise.
2 Related Work
Related work situates the study within zero-shot learning, scaling large language models, and chain-of-thought prompting. It also frames ChatGPT as a model whose zero-shot abilities should be tested beyond dialogue.
- Large Language Models: Scaling research developed increasingly large language models, including Megatron-turing NLG with 530 billion parameters and Gopher.This literature provides the broader context for advances in LLM task solving.
- Zero-Shot Learning: Zero-shot learning solves unseen tasks without labeled training examples, while recent LLMs have demonstrated strong zero-shot performance.Earlier approaches include model-based and instance-based methods; newer work emphasizes LLMs.
- Study positioning: The paper extends ChatGPT evaluation beyond dialogue to reasoning, inference, question answering, summarization, named entity recognition, and sentiment analysis.Its task formats cover six categories in the illustrated instructions, with reasoning treated separately.
- Chain-of-Thought Prompting: Chain-of-thought prompting induces intermediate reasoning steps, with manual-CoT and zero-shot-CoT distinguished by whether demonstrations are provided.Related work also explores decomposition, reasoning-chain quality, multilingual settings, and smaller models.
3 Methodology
The methodology compares ChatGPT and GPT-3.5 using instruction-based zero-shot prompting, with a separate two-stage zero-shot chain-of-thought procedure for reasoning tasks.
- Zero-shot prompting: In single-stage prompting, the model receives a task instruction P concatenated with test problem X and generates target text Y = f(P, X).The sentiment-analysis example requires an exact positive or negative output.
- Zero-shot prompting: Task instructions and input formats vary by task category and specify the expected output behavior.The methodology illustrates this format through sentiment analysis and related task templates.
- Zero-shot chain-of-thought: For zero-shot-CoT, the first stage prompts a rationale R, and the second stage uses R with the original input to generate the final answer.A trigger instruction P2 extracts the answer after rationale generation.
4 Experiments
The experiments section introduces the organization of the evaluation: it describes the tasks and datasets before presenting experimental results.
- Experimental setup: The experiments first describe the tasks and datasets, then present the experimental results.
4.1 Tasks and Datasets
The study evaluates ChatGPT and GPT-3.5 on 20 datasets spanning seven representative NLP task categories, using standard dataset splits and task-specific settings.
- 20 datasets cover seven representative task categories, including reasoning, natural language inference, question answering, dialogue, summarization, named entity recognition, and sentiment analysis.
- Table 1 summarizes the datasets and reports the number of test samples for each.
- Reasoning is divided into arithmetic, commonsense, symbolic, and logical reasoning categories.
- The evaluation uses test splits when labels are available, with validation splits for COPA and CommonsenseQA.
4.2 Experimental Results
Across task categories, ChatGPT shows strong results on several reasoning-oriented tasks but remains uneven, with verbosity and sequence-tagging performance limiting its generality.
- Arithmetic Reasoning: ChatGPT outperforms GPT-3.5 on five of six arithmetic reasoning datasets without chain-of-thought and performs better with chain-of-thought in all cases.
- Commonsense, Symbolic, and Logical Reasoning: ChatGPT performs worse than GPT-3.5 in many commonsense, symbolic, and logical reasoning cases, unlike its arithmetic advantage.
- Natural Language Inference: ChatGPT achieves much better zero-shot performance than GPT-3.5, FLAN, T0, and PaLM on the reported natural language inference tasks.
- Question Answering: ChatGPT outperforms GPT-3.5 by over 2% on BoolQ, whose questions require difficult entailment-like inference.
- Summarization: ChatGPT responses average 36.6 words, compared with 23.3 for GPT-3.5 and 20.0 for ground truth, contributing to lower ROUGE scores.
- Named Entity Recognition: On CoNLL03, both models struggle with named entity recognition relative to fine-tuning methods, especially for the Miscellaneous Entity class.
4.3 ChatGPT v.s. Full-Set or Few-Shot Fine-Tuning
ChatGPT is evaluated against previous full-set or few-shot fine-tuning results for each task, and it underperforms those methods in most cases.
- ChatGPT underperforms the best previous full-set or few-shot fine-tuning method in most individual tasks.
5 Conclusion
The study empirically examines ChatGPT’s zero-shot capabilities across a diverse collection of NLP datasets and task categories, finding both strengths and limitations across task types.
- The study evaluates ChatGPT’s zero-shot capabilities on a large, diverse collection of datasets covering representative NLP task categories.
- Table 11 compares ChatGPT, GPT-3.5, and the best investigated previous full-set or few-shot fine-tuning method across different tasks.
- Table 12 reports sentiment-analysis accuracy on SST2 for zero-shot ChatGPT, zero-shot GPT-3.5 and FLAN, and fine-tuned T5-11B.
- ChatGPT performs well on reasoning and dialogue tasks, while still facing challenges on specific tasks such as sequence tagging.
Limitations
The empirical study is limited by dataset and task coverage, reporting choices for comparison models, prompt selection, and the unresolved comparison between zero-shot and few-shot learning.
- Because of ChatGPT’s cost, the study excludes larger-scale datasets and more task categories, which might prevent further insights.
- For models that are not publicly available, the study reports the best result in the corresponding paper, while public-model results use the best prompt found.
- The study leaves unclear how ChatGPT’s few-shot in-context learning compares with its zero-shot learning across different tasks.
A.1 Example Input and Output Pairs of ChatGPT
The appendix presents example input-output pairs for ChatGPT across arithmetic, commonsense, symbolic, and logical reasoning, followed by several classic NLP tasks.
- Reasoning: Examples cover six arithmetic-reasoning datasets, including MultiArith, GSM8K, AddSub, AQUA-RAT, SingleEq, and SVAMP, with and without chain-of-thought.
- Reasoning: Examples cover commonsense, symbolic, and logical reasoning datasets, with separate entries for chain-of-thought variants.
- Classic NLP tasks: The appendix also includes examples for natural language inference, question answering, dialogue, summarization, named entity recognition, and sentiment analysis.
- Classic NLP tasks: The sentiment-analysis examples use the SST2 dataset.