# Primacy Effect of ChatGPT

arXiv: https://arxiv.org/abs/2310.13206 · HTML: https://mlsanity.com/paper/2310.13206/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://mlsanity.com/paper/2201.11990/) (2022)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification](https://mlsanity.com/paper/2001.01536/) (2020)
- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://mlsanity.com/paper/2302.10198/) (2023)
- [MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages](https://mlsanity.com/paper/2204.08582/) (2022)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://mlsanity.com/paper/2303.08559/) (2023)
- [TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task](https://mlsanity.com/paper/2004.14855/) (2020)
- [Mitigating Label Biases for In-context Learning](https://mlsanity.com/paper/2305.19148/) (2023)
- [An Improved Baseline for Sentence-level Relation Extraction](https://mlsanity.com/paper/2102.01373/) (2021)
- [How would Stance Detection Techniques Evolve after the Launch of ChatGPT?](https://mlsanity.com/paper/2212.14548/) (2022)
- [Re-TACRED: Addressing Shortcomings of the TACRED Dataset](https://mlsanity.com/paper/2104.08398/) (2021)
- [Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation](https://mlsanity.com/paper/2305.07375/) (2023)
- [Zero-shot Temporal Relation Extraction with ChatGPT](https://mlsanity.com/paper/2304.05454/) (2023)
- [On the Educational Impact of ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree?](https://mlsanity.com/paper/2303.11146/) (2023)
- [An Empirical Study on Information Extraction using Large Language Models](https://mlsanity.com/paper/2305.14450/) (2023)
- [Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis](https://mlsanity.com/paper/2205.03784/) (2022)
- [Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature](https://mlsanity.com/paper/2302.06474/) (2023)
- [The moral authority of ChatGPT](https://mlsanity.com/paper/2301.07098/) (2023)

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

- [Eliminating Position Bias of Language Models: A Mechanistic Approach](https://mlsanity.com/paper/2407.01100/) (2024)
- [Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell](https://mlsanity.com/paper/2406.14673/) (2024)
- [Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments](https://mlsanity.com/paper/2412.00323/) (2024)
- [Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization](https://mlsanity.com/paper/2405.20252/) (2024)
- [Positional Biases Shift as Inputs Approach Context Window Limits](https://mlsanity.com/paper/2508.07479/) (2025)
- [Self-Consistency Falls Short! The Adverse Effects of Positional Bias on Long-Context Problems](https://mlsanity.com/paper/2411.01101/) (2024)
- [Hidden in the Haystack: Smaller Needles are More Difficult for LLMs to Find](https://mlsanity.com/paper/2505.18148/) (2025)
- [Quantifying Cognitive Bias Induction in LLM-Generated Content](https://mlsanity.com/paper/2507.03194/) (2025)
- [CaRT: Teaching LLM Agents to Know When They Know Enough](https://mlsanity.com/paper/2510.08517/) (2025)
- [Vulnerability of LLMs to Vertically Aligned Text Manipulations](https://mlsanity.com/paper/2410.20016/) (2024)
- [Understanding the Anchoring Effect of LLM with Synthetic Data: Existence, Mechanism, and Potential Mitigations](https://mlsanity.com/paper/2505.15392/) (2025)
- [Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models](https://mlsanity.com/paper/2506.07334/) (2025)
- [Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models](https://mlsanity.com/paper/2505.15489/) (2025)
- [Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models](https://mlsanity.com/paper/2506.14092/) (2025)
- [Emergence of Episodic Memory in Transformers: Characterizing Changes in Temporal Structure of Attention Scores During Training](https://mlsanity.com/paper/2502.06902/) (2025)
- [Emergence of the Primacy Effect in Structured State-Space Models](https://mlsanity.com/paper/2502.13729/) (2025)
- [Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judge](https://mlsanity.com/paper/2505.15240/) (2025)
- [Image First or Text First? Optimising the Sequencing of Modalities in Large Language Model Prompting and Reasoning Tasks](https://mlsanity.com/paper/2410.03062/) (2024)
- [The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discourse](https://mlsanity.com/paper/2410.15182/) (2024)
- [Order Matters! An Empirical Study on Large Language Models' Input Order Bias in Software Fault Localization](https://mlsanity.com/paper/2412.18750/) (2024)
- [LLMs as In-Context Meta-Learners for Model and Hyperparameter Selection](https://mlsanity.com/paper/2510.26510/) (2025)
- [AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications](https://mlsanity.com/paper/2512.20164/) (2025)
- [Social Simulations with Large Language Model Risk Utopian Illusion](https://mlsanity.com/paper/2510.21180/) (2025)
- [Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives](https://mlsanity.com/paper/2604.06091/) (2026)
- [The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment](https://mlsanity.com/paper/2607.05552/) (2026)
- [A Survey on the Real Power of ChatGPT](https://mlsanity.com/paper/2405.00704/) (2024)
- [Temporal Dependencies in In-Context Learning: The Role of Induction Heads](https://mlsanity.com/paper/2604.01094/) (2026)

## Abstract (arXiv)

Instruction-tuned large language models (LLMs), such as ChatGPT, have led to promising zero-shot performance in discriminative natural language understanding (NLU) tasks. This involves querying the LLM using a prompt containing the question, and the candidate labels to choose from. The question-answering capabilities of ChatGPT arise from its pre-training on large amounts of human-written text, as well as its subsequent fine-tuning on human preferences, which motivates us to ask: Does ChatGPT also inherits humans' cognitive biases? In this paper, we study the primacy effect of ChatGPT: the tendency of selecting the labels at earlier positions as the answer. We have two main findings: i) ChatGPT's decision is sensitive to the order of labels in the prompt; ii) ChatGPT has a clearly higher chance to select the labels at earlier positions as the answer. We hope that our experiments and analyses provide additional insights into building more reliable ChatGPT-based solutions. We release the source code at https://github.com/wangywUST/PrimacyEffectGPT.