# Pushing the Limits of ChatGPT on NLP Tasks

arXiv: https://arxiv.org/abs/2306.09719 · HTML: https://mlsanity.com/paper/2306.09719/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 115 of its 169 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 30 of 115)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)

## Built on by (most cited first, 27 of 27)

- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging](https://mlsanity.com/paper/2406.15479/) (2024)
- [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025)
- [When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions](https://mlsanity.com/paper/2306.15546/) (2023)
- [Reinforcement Learning Enhanced LLMs: A Survey](https://mlsanity.com/paper/2412.10400/) (2024)
- [A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction](https://mlsanity.com/paper/2310.11761/) (2023)
- [Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance](https://mlsanity.com/paper/2402.12819/) (2024)
- [Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness](https://mlsanity.com/paper/2401.15127/) (2024)
- [Are Human-generated Demonstrations Necessary for In-context Learning?](https://mlsanity.com/paper/2309.14681/) (2023)
- [Sentiment Analysis through LLM Negotiations](https://mlsanity.com/paper/2311.01876/) (2023)
- [Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction](https://mlsanity.com/paper/2401.17716/) (2024)
- [Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences](https://mlsanity.com/paper/2309.06578/) (2023)
- [LLMs' Classification Performance is Overclaimed](https://mlsanity.com/paper/2406.16203/) (2024)
- [Automatic Combination of Sample Selection Strategies for Few-Shot Learning](https://mlsanity.com/paper/2402.03038/) (2024)
- [Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection](https://mlsanity.com/paper/2503.21127/) (2025)
- [A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection](https://mlsanity.com/paper/2412.11851/) (2024)
- [Assessing the nature of large language models: A caution against anthropocentrism](https://mlsanity.com/paper/2309.07683/) (2023)
- [Sim-GPT: Text Similarity via GPT Annotated Data](https://mlsanity.com/paper/2312.05603/) (2023)
- [On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic Choices](https://mlsanity.com/paper/2402.12817/) (2024)
- [AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction](https://mlsanity.com/paper/2406.10432/) (2024)
- [Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis](https://mlsanity.com/paper/2410.06550/) (2024)
- [LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models](https://mlsanity.com/paper/2506.05385/) (2025)
- [Exploring the Capabilities of ChatGPT in Ancient Chinese Translation and Person Name Recognition](https://mlsanity.com/paper/2312.15304/) (2023)
- [Assisting humans in complex comparisons: automated information comparison at scale](https://mlsanity.com/paper/2404.04351/) (2024)
- [Instruction Learning Paradigms: A Dual Perspective on White-box and Black-box LLMs](https://mlsanity.com/paper/2506.21573/) (2025)
- [MEKiT: Multi-source Heterogeneous Knowledge Injection Method via Instruction Tuning for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2507.14887/) (2025)
- [MIN-Merging: Merge the Important Neurons for Model Merging](https://mlsanity.com/paper/2510.17890/) (2025)

## Abstract (arXiv)

Despite the success of ChatGPT, its performances on most NLP tasks are still well below the supervised baselines. In this work, we looked into the causes, and discovered that its subpar performance was caused by the following factors: (1) token limit in the prompt does not allow for the full utilization of the supervised datasets; (2) mismatch between the generation nature of ChatGPT and NLP tasks; (3) intrinsic pitfalls of LLMs models, e.g., hallucination, overly focus on certain keywords, etc. In this work, we propose a collection of general modules to address these issues, in an attempt to push the limits of ChatGPT on NLP tasks. Our proposed modules include (1) a one-input-multiple-prompts strategy that employs multiple prompts for one input to accommodate more demonstrations; (2) using fine-tuned models for better demonstration retrieval; (3) transforming tasks to formats that are more tailored to the generation nature; (4) employing reasoning strategies that are tailored to addressing the task-specific complexity; (5) the self-verification strategy to address the hallucination issue of LLMs; (6) the paraphrase strategy to improve the robustness of model predictions. We conduct experiments on 21 datasets of 10 representative NLP tasks, including question answering, commonsense reasoning, natural language inference, sentiment analysis, named entity recognition, entity-relation extraction, event extraction, dependency parsing, semantic role labeling, and part-of-speech tagging. Using the proposed assemble of techniques, we are able to significantly boost the performance of ChatGPT on the selected NLP tasks, achieving performances comparable to or better than supervised baselines, or even existing SOTA performances.