The paper titled "Primacy Effect of ChatGPT" explores whether large language models like ChatGPT inherit humans' cognitive biases. To investigate this, the authors conduct experiments using ChatGPT and analyze its decision-making process based on the order of labels in the prompt. They find that ChatGPT's decision is sensitive to the order of labels and has a higher likelihood of selecting labels at earlier positions as the answer. To evaluate this bias quantitatively, they introduce a metric called label order imbalance (LOI), which measures the disparity between predicted label indices and a uniform distribution. The results show that ChatGPT exhibits unfair treatment of label indices when making relation label predictions for input texts and that this unfairness increases with task difficulty. Furthermore, the researchers release their source code to facilitate further exploration and development in this area. Overall, this research contributes to our understanding of how large language models like ChatGPT make decisions and emphasizes the importance of addressing biases in these models for more trustworthy outcomes.
- The paper explores if large language models like ChatGPT inherit human cognitive biases
- Experiments were conducted using ChatGPT to analyze its decision-making process based on label order in the prompt
- ChatGPT's decision is sensitive to label order and has a higher likelihood of selecting labels at earlier positions as the answer
- A metric called label order imbalance (LOI) was introduced to quantitatively evaluate this bias
- Results show that ChatGPT exhibits unfair treatment of label indices, particularly for relation label predictions and with increasing task difficulty
- Source code is released by the researchers for further exploration and development in this area
- This research contributes to understanding how large language models make decisions and highlights the need to address biases for more trustworthy outcomes.
The paper talks about whether big language models like ChatGPT have the same biases as humans. They did experiments with ChatGPT to see how it makes decisions based on the order of labels in the question. The results showed that ChatGPT is more likely to choose labels that come earlier in the question. They introduced a metric called label order imbalance (LOI) to measure this bias. The results also showed that ChatGPT treats certain label predictions unfairly, especially for relation labels and harder tasks. The researchers released the source code for others to study and improve upon. This research helps us understand how big language models make decisions and reminds us to address biases for better outcomes."
Definitions
- Language models: Programs or systems that can understand and generate human-like language.
- Biases: Unfair preferences or prejudices towards certain things or groups.
- Experiments: Tests or trials done to gather information or prove something.
- Decision-making process: The steps taken to choose an answer or make a choice.
- Label order: The arrangement or sequence of options in a question.
- Metric: A way of measuring or evaluating something.
- Quantitatively: Using numbers and data to describe something.
- Unfair treatment: Treating something or someone in a way that is not fair or equal.
- Source code: The instructions written by programmers that make up a computer program.
Exploring Cognitive Biases in Large Language Models: Primacy Effect of ChatGPT
Large language models (LLMs) have become increasingly popular for natural language processing tasks, such as text classification and relation extraction. However, these models may inherit humans’ cognitive biases, which can lead to unfair or biased decisions. In this paper, the authors explore whether LLMs like ChatGPT exhibit a primacy effect – that is, whether they are more likely to select labels at earlier positions when making predictions.
Background
Humans often exhibit a primacy effect – that is, they tend to remember information presented at the beginning of a list better than information presented later on. This phenomenon has been studied extensively in psychology and cognitive science research. The authors hypothesize that large language models like ChatGPT may also exhibit this bias when making decisions based on input texts. To test their hypothesis, they conduct experiments using ChatGPT and analyze its decision-making process based on the order of labels in the prompt.
Experiments and Results
The authors use two datasets – one for relation label prediction (RLP) task and another for sentiment analysis (SA) task – to evaluate how sensitive ChatGPT is to label order in prompts. For each dataset, they create four different versions with different label orders and compare the results across all four versions. They find that ChatGPT's decision is indeed sensitive to the order of labels; it has a higher likelihood of selecting labels at earlier positions as the answer compared to those at later positions regardless of task difficulty level or dataset type used.
To quantify this bias further, they introduce a metric called label order imbalance (LOI), which measures the disparity between predicted label indices and a uniform distribution across all possible indices in each version of data set used during evaluation phase . The results show that LOI values increase with task difficulty level; thus indicating an increased unfairness towards certain label indices when making predictions by ChatGPT model .
Conclusion
Overall , this research contributes significantly towards our understanding about how large language models make decisions , especially highlighting their sensitivity towards human cognitive biases . It emphasizes upon importance of addressing such biases while developing AI systems so as to ensure trustworthy outcomes from them . Furthermore , researchers have released source code related to this work which would facilitate further exploration into this area .
