Summary Crowd workers use LLMs for text tasks. arxiv.org
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The text discusses the challenges of detecting and validating the use of large language models (LLMs) by crowd workers, highlights their impact on various tasks, and suggests the use of copy-pasting as a means to identify synthetic or human-written summaries.
Key Points
- Large language models (LLMs) are being used by crowd workers for various text tasks.
- The use of LLMs raises concerns about data quality, collaboration between workers and AI, detection of AI-generated text, privacy concerns, and cognitive biases in crowdsourcing.
- The prevalence of LLM usage among crowd workers is estimated to be between 33-46%.
- Detecting the usage of LLMs is challenging but can be done through methods such as keystroke detection and synthetic-vs.-real classification.
- Human expertise and data obtained from real humans are still critical for obtaining reliable results in text production tasks.
- It is important to understand the limitations and potential biases of LLMs to ensure the reliability of data obtained through crowdsourcing.
Summaries
166 word summary
Large language models (LLMs) are widely used by researchers and industry practitioners for data creation, annotation, and summarization. Detecting LLM usage is challenging but important for ensuring the reliability of crowdsourced data. The text discusses the use of LLMs by crowd workers for text tasks and mentions a study on weight loss that found no advantage for any specific diet. Another study estimated that 33-46% of crowd workers use LLMs based on submitted summaries and developed a classification model to identify synthetic abstracts. The text also addresses privacy concerns, user interactions with keystroke tracking, and the validation of annotation tasks. It calls for further research to understand the effects of LLMs on different tasks and highlights their use in the education space and by crowd workers on platforms like MTurk. The overlap between summaries produced by crowd workers and original abstracts is noted, suggesting the use of copy-pasting. A classifier was able to identify synthetic or human-written summaries based on copy-pasting with a low false-positive rate.
387 word summary
This text excerpt discusses the use of large language models (LLMs) by crowd workers for text tasks. It references various studies and preprints related to LLMs in text generation, chatbot evaluation, dataset creation, and data annotation. The text also mentions the collaboration between workers and AI, the detection of AI-generated text, and the question of whose opinions language models reflect. Privacy concerns, user interactions with keystroke tracking, and the validation of annotation tasks are discussed. The study raises concerns about the impact of LLMs on acquiring human data and the potential degradation of performance. It calls for further research to understand the effects of LLMs on different tasks. The study also highlights the use of LLMs in the education space and the widespread use of LLMs by crowd workers on platforms like MTurk. The overlap between summaries produced by crowd workers and original abstracts is noted, suggesting the use of copy-pasting, but not necessarily synthetic text. A classifier was able to identify synthetic or human-written summaries based on copy-pasting, with a low false-positive rate. Analysis showed that most users pasted some text when writing their summaries. A study estimated that 33-46% of crowd workers use large language models (LLMs) based on submitted summaries. A classification model was used to identify synthetic abstracts, with 21 out of 46 being classified as synthetic. The study trained a model using abstracts and summaries from the New England Journal of Medicine and found high accuracy in detecting synthetic text. The study also mentions the need for high-quality summaries and manual inspection.
The text also discusses the use of LLMs by crowd workers for text tasks. It mentions a study on weight loss where attendance at group sessions was strongly associated with success. The diets had similar effects on lipid-satiety, hunger, satisfaction, and attendance. The study randomly assigned overweight adults to four different diets but did not establish an advantage for any specific diet.
LLMs are popular tools used by researchers and industry practitioners for data creation, annotation, and summarization. Concerns about the reliability of results obtained from LLMs have been raised, with a case study showing that a significant percentage of crowd workers use LLMs. Detecting LLM usage is challenging but important for ensuring the reliability of crowdsourced data. Understanding the capabilities and biases of LLMs is crucial in this regard.
847 word summary
LLMs are popular tools used by researchers and industry practitioners for creating, annotating, and summarizing data. However, there are concerns about the reliability and validity of results obtained from LLMs. A case study found that 33-46% of crowd workers use LLMs when completing tasks, raising questions about the quality of annotations and the data obtained through crowdsourcing. Detecting LLM usage is challenging but important for those who rely on crowdsourced data. It is crucial to understand the capabilities and potential biases of LLMs in order to ensure the reliability of data obtained through crowdsourcing. The text discusses the use of LLMs by crowd workers for text tasks. It mentions that attendance at group sessions was strongly associated with weight loss. The diets improved lipid-Satiety, hunger, satisfaction with the diet, and attendance at group sessions were similar for all diets. The study randomly assigned 811 overweight adults to four different diets with targeted percentages of energy derived from fat, protein, and carbohydrates. The advantage of a diet emphasizing protein, fat, or carbohydrates for weight loss has not been established. The text also mentions the need for high-quality summaries and manual inspection, and the detection of synthetic text using AI classifiers.
The study focuses on the use of large language models (LLMs) for text tasks, specifically in detecting synthetic text. The researchers trained a model using abstracts and summaries from the New England Journal of Medicine. They found that the model was able to accurately identify synthetic text, even when it had not been exposed to certain abstracts during training. The study utilized both abstract-level and summary-level splits to evaluate performance. The results showed high accuracy and F1 scores for the synthetic-text detection model. The researchers also used a custom solution to fine-tune their model.
The prevalence of LLM usage among crowd workers was estimated through post-hoc validation. The fraction of LLM-using crowd workers was found to be 33-46% based on the submitted summaries. A logit threshold was used to classify summaries as synthetic or human-generated, and it was found that 21 out of 46 crowdsourced summaries were classified as synthetic. The classification model showed a low false-positive rate and high accuracy in identifying synthetic abstracts.
LLMs are being used in various settings, including the education space, where synthetic text can pose challenges. Bespoke detection methods may be more effective than out-of-the-box solutions. Crowd workers on platforms like MTurk widely use LLMs. The overlap between summaries produced by crowd workers and original abstracts suggests that copy-pasting is commonly used, but it does not necessarily imply the usage of synthetic text. A classifier identified all summaries as synthetic or human-written based on the presence of copy-pasting. The classifier had a low false-positive rate. Analysis showed that the majority of users pasted at least some text when writing their summaries.
Overall, the study explores the use of LLMs by crowd workers for text tasks and highlights the need for further research to understand their impact and limitations. The use of Language Model Models (LLMs) by crowd workers for text tasks is examined in this text excerpt. The study raises concerns about the impact of LLMs on acquiring human data and the potential degradation of performance. It acknowledges the limitations of focusing on a specific task (text summarization) and calls for research on other tasks. The researchers speculate that the phenomenon uncovered in this study may become more widespread in the future. The need for further research to understand how LLMs affect different types of tasks and how they evolve over time is highlighted.
The text also discusses various other aspects related to the use of LLMs by crowd workers for text tasks. This includes cognitive biases in crowdsourcing, collaboration between workers and AI, detection of AI-generated text, privacy concerns, and demographics of mechanical turk workers. It references the AAAI Conference on Human Computation and Crowdsourcing.
Additionally, the text mentions the potential privacy concerns and user interactions with keystroke tracking, as well as the validation of annotation tasks using keystroke collection. It discusses message distortion in information cascades, the use of language models to simulate human samples, and the curse of recursion in training on generated data.
The text references a watermark for large language models, human heuristics for AI-generated language, and large language models as simulated economic agents. It also discusses the collaboration between workers and AI, the detection of AI-generated text, and the question of whose opinions language models reflect. A multi-group analysis for text summarization and the use of ChatGPT as a factual inconsistency evaluator are mentioned. The use of LLMs by crowd workers for text tasks is discussed, along with the use of a regular USA consumer panel and MTurk samples for online survey respondent data quality.
Overall, this text excerpt provides a list of references and citations from various studies and preprints related to the use of large language models (LLMs) in text tasks. These studies cover a range of topics, including text generation, chatbot evaluation, dataset creation, data annotation, and more. The studies mentioned in the text are from various years, including 2021, 2022, and 2023.
1449 word summary
The excerpted text is a list of references and citations from various studies and preprints related to the use of large language models (LLMs) in text tasks. These studies cover a range of topics, including text generation, chatbot evaluation, dataset creation, data annotation, and more. The references include titles such as "Can large language models transform computational social science?", "A survey of controllable text generation using transformer-based pre-trained language models", and "Shifting attention to accuracy can reduce misinformation pollution with large language models". The studies mentioned in the text are from various years, including 2021, 2022, and 2023. Paragraph 1: The text discusses the use of LLMs (large language models) by crowd workers for text tasks. It mentions the use of a regular USA consumer panel and MTurk samples for online survey respondent data quality.
Paragraph 2: The text references a multi-group analysis for text summarization and the use of ChatGPT as a factual inconsistency evaluator. It also mentions the curse of recursion in training on generated data.
Paragraph 3: The text discusses the collaboration between workers and AI, the detection of AI-generated text, and the question of whose opinions language models reflect.
Paragraph 4: The text references a watermark for large language models and human heuristics for AI-generated language. It also mentions large language models as simulated economic agents.
Paragraph 5: The text discusses message distortion in information cascades, the use of language models to simulate human samples, and the demographics of mechanical turk workers.
Paragraph 6: The text mentions the potential privacy concerns and user interactions with keystroke tracking, as well as the validation of annotation tasks using keystroke collection.
Paragraph 7: The text references cognitive biases in crowdsourcing and the AAAI Conference on Human Computation and Crowdsourcing.
Overall, the text excerpt discusses various aspects related to the use of LLMs by crowd workers for text tasks, including data quality, collaboration between workers and AI, detection of AI-generated text, privacy concerns, and cognitive biases in crowdsourcing. This summary focuses on the key points and important details from the excerpted text.
Paragraph 1: The study examines the use of Language Model Models (LLMs) by crowd workers for text tasks.
Paragraph 2: The researchers speculate that the phenomenon uncovered in this study may become more widespread in the future.
Paragraph 3: The study highlights the need for further research to understand how LLMs affect different types of tasks and how they evolve over time.
Paragraph 4: The study acknowledges the limitations of focusing on a specific task (text summarization) and calls for research on other tasks.
Paragraph 5: The study raises concerns about the impact of LLMs on acquiring human data and the potential degradation of performance.
Overall, the study explores the use of LLMs by crowd workers for text tasks and highlights the need for further research to understand their impact and limitations. LLMs are being used in various settings, including the education space, where synthetic text can pose challenges. Bespoke detection methods may be more effective than out-of-the-box solutions. Crowd workers on platforms like MTurk widely use LLMs. The overlap between summaries produced by crowd workers and original abstracts suggests that copy-pasting is commonly used, but it does not necessarily imply the usage of synthetic text. A classifier identified all summaries as synthetic or human-written based on the presence of copy-pasting. The classifier had a low false-positive rate. Analysis showed that the majority of users pasted at least some text when writing their summaries. The prevalence of LLM usage among crowd workers was estimated through post-hoc validation. The fraction of LLM-using crowd workers was found to be 33-46% based on the submitted summaries. A logit threshold was used to classify summaries as synthetic or human-generated, and it was found that 21 out of 46 crowdsourced summaries were classified as synthetic. The classification model showed a low false-positive rate and high accuracy in identifying synthetic abstracts. The study focuses on the use of large language models (LLMs) for text tasks, specifically in detecting synthetic text. The researchers trained a model using abstracts and summaries from the New England Journal of Medicine. They found that the model was able to accurately identify synthetic text, even when it had not been exposed to certain abstracts during training. The study utilized both abstract-level and summary-level splits to evaluate performance. The results showed high accuracy and F1 scores for the synthetic-text detection model. The researchers also used a custom solution, rather than relying on API calls, to fine-tune their model. Overall, the study demonstrates the effectiveness of LLMs in detecting synthetic text and highlights the potential for future large-scale datasets. The text discusses the use of LLMs (Language Models) by crowd workers for text tasks. It mentions that attendance at group sessions was strongly associated with weight loss. The diets improved lipid-Satiety, hunger, satisfaction with the diet, and attendance at group sessions were similar for all diets. The study randomly assigned 811 overweight adults to four different diets with targeted percentages of energy derived from fat, protein, and carbohydrates. The advantage of a diet emphasizing protein, fat, or carbohydrates for weight loss has not been established. The text also mentions the need for high-quality summaries and manual inspection, and the detection of synthetic text using AI classifiers. The goal is to detect whether crowd workers' answers are synthetic or original. The experiment involves summarizing medical research papers from the New England Journal of Medicine and studying the "telephone effect" where information is gradually lost or distorted as it is passed from human to human. The process is iterated with summaries and the task is reduced to a single summarization step. The text also mentions the use of keyboard shortcuts and estimates that the task would take around 4 minutes per summary. 48 summaries were obtained from 44 distinct workers, who were paid $1 per summary. Crowd workers use LLMs for text tasks. The study focuses on how information is lost when humans summarize text. The chosen task for the study is abstract summarization. The study illustrates the overall approach in Figure 1 and describes the methods used. There are concerns about the usage of LLMs, such as cheating assignments and exams. Detecting LLM-generated data is difficult, which has led to concerns about their usage in areas such as social media. Previous research has shown that LLMs can act as effective proxies for human submissions. Research using crowdsourcing platforms has shed light on the demographics and socioeconomic conditions of crowd workers. There is a rich body of literature on crowdsourcing, and previous work has studied the limitations and overall quality of crowdsourced annotations. Crowd workers are using Language Models (LLMs) for text tasks, which has led to a shift in how machine learning datasets are created. LLMs are being used for tasks such as transcription, image annotation, and text summarization. The prevalence of LLM usage among crowd workers is estimated to be between 33-46%. Detecting the usage of LLMs is challenging, but a method combining keystroke detection and synthetic-vs.-real classification has been developed. Understanding the extent to which crowd workers rely on LLMs is important for those who depend on crowdsourced data. If crowd workers are using LLMs instead of human-written responses, it could diminish the utility of crowdsourced data. Large language models (LLMs) like ChatGPT and GPT-4 have become popular tools for researchers and industry practitioners. They offer ways to create, annotate, and summarize data, and have shown promise in simulating human behavior. However, there are concerns about the validity of results obtained from LLMs, as they can still be unfaithful with respect to tasks and perform poorly in various experiments. Human expertise and data obtained from real humans are still critical for obtaining reliable results.
Crowd workers, who rely on platforms like Amazon Mechanical Turk and Prolific, have also started using LLMs to increase their productivity and income. However, the use of LLMs by crowd workers raises concerns about the impact on the quality of annotations and the validity of results. It is important to understand the capabilities of LLMs and the potential biases they introduce in order to ensure the reliability of data obtained through crowdsourcing.
In order to investigate the prevalence of LLM usage by crowd workers, a case study was conducted. The study found that 33-46% of crowd workers used LLMs when completing tasks. This raises questions about the reliability of annotations and the potential impact of LLMs on the quality of data obtained through crowdsourcing.
Overall, while LLMs offer new opportunities for researchers and industry practitioners, it is important to be cautious about their limitations and potential biases. Human expertise and data obtained from real humans are still crucial for obtaining reliable results in text production tasks.