CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis

Saranya Venkatraman, Nafis Irtiza Tripto, Dongwon Lee·June 18, 2024

Summary

The paper introduces CollabStory, a novel dataset for multi-LLM collaborative story generation, involving up to five open-source instruction-tuned LLMs. It investigates LLM-LLM collaboration, addressing challenges such as plagiarism detection, credit assignment, and academic integrity. The dataset, created by combining stories from Gemma, Llama, Mistral, Orca, and Olmo, simulates human-LLM interactions and aims to spur research on discerning contributions and implications of machine-machine collaboration in storytelling. Studies analyze the dataset's properties, comparing it to human-written stories, and evaluate various machine learning methods for tasks like authorship detection and prediction. The work highlights the need for new techniques to address the unique challenges in LLM collaboration and the evolving landscape of generative AI, emphasizing the importance of attributing ownership and maintaining text integrity.

Key findings

4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis" aims to explore collaborative writing scenarios involving multiple Large Language Models (LLMs) by creating a dataset called CollabStory . This paper addresses the lack of efforts in investigating multi-LLM collaboration for open-ended tasks, which is a relatively new problem in the field of natural language processing and machine learning . The study extends authorship-related tasks from existing frameworks to analyze multi-LLM settings and presents baselines for LLM-LLM collaboration, highlighting the need to understand and develop techniques for utilizing multiple LLMs effectively .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the collaborative creative story-writing scenario involving multiple open-source Large Language Models (LLMs) . The study focuses on exploring the challenges and implications of LLM-LLM collaboration in generating creative stories, addressing issues such as plagiarism detection, credit assignment, maintaining academic integrity, and copyright concerns . The research seeks to demonstrate how multiple LLMs can collaborate to co-author creative stories, highlighting the unique hurdles and opportunities presented by this emerging scenario .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis" proposes several new ideas, methods, and models related to collaborative story generation and authorship analysis using Large Language Models (LLMs) .

  1. Authorship Analysis Extension: The paper extends the PAN tasks for multi-LLM scenarios, focusing on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection . This extension aims to address the challenge of analyzing multi-authored text among humans by establishing benchmarks for authorship-related tasks using LLMs .

  2. Performance Comparison: The study compares the performance of different baseline methods such as Multinomial Naive Bayes, Support Vector Machine, BERT, ALBERT, and RoBERTa in predicting whether a story is written by multiple authors or not . The performance is evaluated based on the number of authors involved in writing the story, ranging from 1 to 5 .

  3. Collaborative Story Generation: The research explores collaborative story generation by up to 5 LLMs, where each story segment is generated by a single author who then passes the narrative to the next author, completing the storyline sequentially . This collaborative approach involves LLMs seamlessly collaborating and handing off tasks to one another without external routing algorithms, potentially leading to automated writing assistants working together without human intervention .

  4. Authorship Attribution: The paper introduces tasks for Authorship Attribution, where different methods like MNB, SVM, BERT, ALBERT, and RoBERTa are used to attribute authorship to different LLMs involved in writing the collaborative stories . The study evaluates the performance of these methods based on the number of authors contributing to the story, ranging from 1 to 4 .

In summary, the paper introduces innovative approaches to collaborative story generation, authorship analysis, and attribution using LLMs, aiming to address the complexities of multi-authored text generation and establish benchmarks for analyzing authorship in collaborative writing scenarios . The paper "CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis" introduces several characteristics and advantages compared to previous methods in the field of collaborative story generation and authorship analysis using Large Language Models (LLMs) .

  1. Extension of Authorship Analysis Tasks: The study extends the PAN tasks for multi-LLM scenarios, focusing on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection, which have been persistent challenges in analyzing multi-authored text among humans for over 15 years . This extension aims to establish benchmarks for authorship-related tasks using LLMs, providing a more comprehensive approach to analyzing collaborative writing scenarios.

  2. Performance Comparison with Baseline Methods: The research compares the performance of different baseline methods such as Multinomial Naive Bayes, Support Vector Machine, BERT, ALBERT, and RoBERTa in predicting whether a story is written by multiple authors or not . The study evaluates the performance based on the number of authors involved in writing the story, ranging from 1 to 5, providing insights into the effectiveness of these methods in multi-authored text analysis.

  3. Fine-Tuning and Reporting Performance: The paper fine-tunes and reports the performance of the baseline methods using various descriptive features such as vocabulary richness, % of stopwords, readability, entropy, and coherence . This detailed analysis allows for a comprehensive evaluation of the effectiveness of different methods in handling multi-authored text scenarios.

  4. Addressing Nuanced Authorship Concerns: The study raises nuanced authorship concerns in the context of collaborative story generation using LLMs, questioning who should be considered the true creative source in such scenarios . It delves into questions of crediting all LLMs involved, acknowledging human developers, and determining ownership based on factors like word count, narrative depth, or plot twists, contributing to a deeper understanding of authorship in collaborative writing settings.

  5. Real-World Implications and Future Work: The research introduces authorship-related tasks using CollabStory to accurately discern the usage of multiple LLMs in texts, addressing concerns related to ownership and proving the origins of creative work . The extension of PAN-inspired authorship tasks is closely linked to real-world implications, paving the way for further exploration of collaborative writing scenarios and authorship attribution in the context of generative AI.

In summary, the paper's detailed analysis, extension of authorship tasks, performance comparison with baseline methods, and exploration of nuanced authorship concerns contribute significantly to advancing the understanding of collaborative story generation and authorship analysis using LLMs .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of collaborative story generation and authorship analysis. Noteworthy researchers in this area include Zhong et al. , who utilized "writing modes" as a control signal for better alignment during co-writing with humans, and CoAuthor, which positioned GPT3.5 as a writing collaborator for over 50 human participants . Additionally, the CollabStory dataset was developed by a team that includes authors such as Groeneveld, Mukherjee, and others .

The key to the solution mentioned in the paper revolves around the creation of a dataset called CollabStory, which simulates scenarios where different open-source Large Language Models (LLMs) collaboratively work on a storyline, passing control of the story from one LLM to the next. This dataset enables the study of collaborative creative story writing involving multiple LLMs, addressing challenges related to plagiarism detection, credit assignment, maintaining academic integrity, and copyright concerns . The dataset contains stories written by up to five LLMs, with a focus on machine-machine collaboration .


How were the experiments in the paper designed?

The experiments in the paper were designed to explore collaborative creative story-writing scenarios involving multiple Language Model Models (LLMs) . The study focused on single-author to multi-author scenarios, where up to five LLMs co-authored creative stories . The researchers aimed to address challenges related to plagiarism detection, credit assignment, maintaining academic integrity, and addressing copyright infringement concerns in the context of LLM-LLM collaboration . The dataset created for the experiments, called CollabStory, involved using five frequently used LLMs to simulate a scenario where LLMs from different organizations collaboratively work on a storyline . The stories in the dataset varied in the number of authors/LLMs involved, ranging from being written by a single LLM to collaboratively written by up to all five LLMs . The experiments aimed to demonstrate the potential of combining the expertise of LLMs specialized in various tasks for collaborative story generation and authorship analysis .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is called CollabStory, which is the first exclusively LLM-generated collaborative story dataset . The code for this dataset is open source and available at the following GitHub link: https://github.com/saranya-venkatraman/multi_llm_story_writing .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study focuses on collaborative creative story-writing scenarios involving multiple Language Model Models (LLMs) collaborating with each other . The dataset created, CollabStory, is the largest publicly available dataset of creative stories written collaboratively by different LLMs . The methodology involved generating stories using five open-source instruction-tuned LLMs, simulating a scenario where LLMs from different sources collaboratively work on a storyline . This approach aligns with the hypothesis that multi-LLM collaboration can lead to unique challenges and opportunities in creative writing tasks .

The study demonstrates the implications of Multi-LLM settings for various stakeholders, including LLM developers and end-users, highlighting considerations such as credit assignment and the legality of usage in the generative AI landscape . By replicating tasks from previous works and setting new baselines for authorship-related tasks in LLM-LLM collaboration, the paper addresses the emerging challenges posed by machine-machine collaboration . This analysis supports the hypothesis that current baselines are indeed challenged by the complexities of multi-LLM collaboration in text generation tasks .

Furthermore, the post-processing and filtering steps applied to the dataset ensure the quality and integrity of the generated stories, aligning with the hypothesis that maintaining academic integrity, addressing copyright concerns, and ensuring coherence in collaborative writing are crucial aspects to consider in multi-LLM settings . The descriptive statistics provided in the study offer insights into the differences between LLM-coauthored and human-written stories, supporting the hypothesis that collaborative writing with LLMs presents unique challenges that need to be addressed .

In conclusion, the experiments and results presented in the paper offer comprehensive support for the scientific hypotheses related to multi-LLM collaborative story generation and authorship analysis. The study's methodology, findings, and analyses contribute significantly to understanding the implications and challenges of machine-machine collaboration in creative writing tasks, validating the importance of exploring new techniques to navigate the complexities of multi-LLM settings in text generation .


What are the contributions of this paper?

The paper "CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis" makes several significant contributions:

  • It introduces the concept of Multi-LLM collaborative story-writing scenarios, where multiple Language Model Models (LLMs) collaborate to create coherent storylines .
  • The paper presents the CollabStory dataset, which is the largest publicly available dataset for creative stories collaboratively written by different LLMs, enabling the study of machine-machine collaboration in generating text .
  • It addresses the challenges and considerations involved in multi-LLM settings, such as credit assignment, legality of usage, and the implications for various stakeholders like LLM developers and end-users .
  • The study explores the potential of combining the expertise of LLMs specialized in various tasks, highlighting the emergence of LLMs conversing for continuous generative tasks in open domains .
  • The paper discusses the importance of developing methods to tackle the challenges posed by machine-machine collaboration, such as the potential misuse by malicious actors to spread misinformation .
  • It provides insights into the implications of LLM-LLM collaboration for addressing ongoing challenges related to plagiarism detection, credit assignment, maintaining academic integrity, and copyright concerns in educational settings .

What work can be continued in depth?

Further research in the field of collaborative story generation and authorship analysis can delve deeper into several aspects based on the provided context:

  • Exploration of Multi-LLM Settings: Future studies can explore the implications of Multi-LLM settings for different stakeholders, such as LLM developers and end-users, focusing on considerations like credit assignment and legality of usage .
  • Enhancing Collaborative Writing Scenarios: Research can focus on enhancing collaborative creative story-writing scenarios involving multiple LLMs, addressing challenges like plagiarism detection, credit assignment, and maintaining academic integrity .
  • Dataset Development: There is a need to develop more datasets that can be leveraged to understand collaborative story writing, similar to the STORIUM dataset, which contains creative stories written through human-human collaboration .
  • Ethical Considerations: Future work should also address ethical considerations related to using LLMs for creative story writing, including potential biases and harmful stereotypes present in the LLMs' original training data, transparency in content attribution, and guidelines to ensure LLMs enhance human creativity without undermining it .
  • Comparison with Existing Datasets: Researchers can further compare CollabStory with other existing collaborative creative story datasets to identify strengths, weaknesses, and areas for improvement in collaborative story generation tasks .

Tables

8

Introduction
Background
Emergence of instruction-tuned LLMs
Growing interest in machine-machine collaboration
Objective
To develop and analyze CollabStory dataset
Investigate plagiarism detection, credit assignment, and academic integrity in LLM collaboration
Spur research on human-LLM interaction and machine-generated content
Method
Data Collection
Source Stories
Gemma, Llama, Mistral, Orca, and Olmo LLMs
Human-written stories for comparison
Combination Process
Merging and adapting stories for collaboration scenarios
Data Preprocessing
Cleaning and formatting for LLM compatibility
Annotation of collaboration patterns and authorship
Splitting into training, validation, and test sets
Dataset Analysis
Properties and Characteristics
Size and diversity of stories
Complexity of collaborative structures
Comparison with human-written stories
Tasks and Evaluation
Authorship Detection
Methods and performance metrics
Prediction of Collaboration Dynamics
Machine learning models and results
Challenges and Implications
Plagiarism Detection
Techniques and limitations
Credit Assignment
Attribution methods for LLM collaboration
Academic Integrity
Ethical considerations and future guidelines
Conclusion
The need for new research techniques
Impact on generative AI and text integrity
Directions for future work in LLM collaboration
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
How many open-source LLMs are involved in the multi-LLM collaborative story generation process?
What is the main objective of the studies analyzing the CollabStory dataset?
What is the primary focus of the CollabStory dataset introduced in the paper?
What are some of the challenges investigated in the paper regarding LLM-LLM collaboration?

CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis

Saranya Venkatraman, Nafis Irtiza Tripto, Dongwon Lee·June 18, 2024

Summary

The paper introduces CollabStory, a novel dataset for multi-LLM collaborative story generation, involving up to five open-source instruction-tuned LLMs. It investigates LLM-LLM collaboration, addressing challenges such as plagiarism detection, credit assignment, and academic integrity. The dataset, created by combining stories from Gemma, Llama, Mistral, Orca, and Olmo, simulates human-LLM interactions and aims to spur research on discerning contributions and implications of machine-machine collaboration in storytelling. Studies analyze the dataset's properties, comparing it to human-written stories, and evaluate various machine learning methods for tasks like authorship detection and prediction. The work highlights the need for new techniques to address the unique challenges in LLM collaboration and the evolving landscape of generative AI, emphasizing the importance of attributing ownership and maintaining text integrity.
Mind map
Machine learning models and results
Methods and performance metrics
Merging and adapting stories for collaboration scenarios
Human-written stories for comparison
Gemma, Llama, Mistral, Orca, and Olmo LLMs
Ethical considerations and future guidelines
Attribution methods for LLM collaboration
Techniques and limitations
Prediction of Collaboration Dynamics
Authorship Detection
Comparison with human-written stories
Complexity of collaborative structures
Size and diversity of stories
Splitting into training, validation, and test sets
Annotation of collaboration patterns and authorship
Cleaning and formatting for LLM compatibility
Combination Process
Source Stories
Spur research on human-LLM interaction and machine-generated content
Investigate plagiarism detection, credit assignment, and academic integrity in LLM collaboration
To develop and analyze CollabStory dataset
Growing interest in machine-machine collaboration
Emergence of instruction-tuned LLMs
Directions for future work in LLM collaboration
Impact on generative AI and text integrity
The need for new research techniques
Academic Integrity
Credit Assignment
Plagiarism Detection
Tasks and Evaluation
Properties and Characteristics
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Challenges and Implications
Dataset Analysis
Method
Introduction
Outline
Introduction
Background
Emergence of instruction-tuned LLMs
Growing interest in machine-machine collaboration
Objective
To develop and analyze CollabStory dataset
Investigate plagiarism detection, credit assignment, and academic integrity in LLM collaboration
Spur research on human-LLM interaction and machine-generated content
Method
Data Collection
Source Stories
Gemma, Llama, Mistral, Orca, and Olmo LLMs
Human-written stories for comparison
Combination Process
Merging and adapting stories for collaboration scenarios
Data Preprocessing
Cleaning and formatting for LLM compatibility
Annotation of collaboration patterns and authorship
Splitting into training, validation, and test sets
Dataset Analysis
Properties and Characteristics
Size and diversity of stories
Complexity of collaborative structures
Comparison with human-written stories
Tasks and Evaluation
Authorship Detection
Methods and performance metrics
Prediction of Collaboration Dynamics
Machine learning models and results
Challenges and Implications
Plagiarism Detection
Techniques and limitations
Credit Assignment
Attribution methods for LLM collaboration
Academic Integrity
Ethical considerations and future guidelines
Conclusion
The need for new research techniques
Impact on generative AI and text integrity
Directions for future work in LLM collaboration
Key findings
4

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis" aims to explore collaborative writing scenarios involving multiple Large Language Models (LLMs) by creating a dataset called CollabStory . This paper addresses the lack of efforts in investigating multi-LLM collaboration for open-ended tasks, which is a relatively new problem in the field of natural language processing and machine learning . The study extends authorship-related tasks from existing frameworks to analyze multi-LLM settings and presents baselines for LLM-LLM collaboration, highlighting the need to understand and develop techniques for utilizing multiple LLMs effectively .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the collaborative creative story-writing scenario involving multiple open-source Large Language Models (LLMs) . The study focuses on exploring the challenges and implications of LLM-LLM collaboration in generating creative stories, addressing issues such as plagiarism detection, credit assignment, maintaining academic integrity, and copyright concerns . The research seeks to demonstrate how multiple LLMs can collaborate to co-author creative stories, highlighting the unique hurdles and opportunities presented by this emerging scenario .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis" proposes several new ideas, methods, and models related to collaborative story generation and authorship analysis using Large Language Models (LLMs) .

  1. Authorship Analysis Extension: The paper extends the PAN tasks for multi-LLM scenarios, focusing on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection . This extension aims to address the challenge of analyzing multi-authored text among humans by establishing benchmarks for authorship-related tasks using LLMs .

  2. Performance Comparison: The study compares the performance of different baseline methods such as Multinomial Naive Bayes, Support Vector Machine, BERT, ALBERT, and RoBERTa in predicting whether a story is written by multiple authors or not . The performance is evaluated based on the number of authors involved in writing the story, ranging from 1 to 5 .

  3. Collaborative Story Generation: The research explores collaborative story generation by up to 5 LLMs, where each story segment is generated by a single author who then passes the narrative to the next author, completing the storyline sequentially . This collaborative approach involves LLMs seamlessly collaborating and handing off tasks to one another without external routing algorithms, potentially leading to automated writing assistants working together without human intervention .

  4. Authorship Attribution: The paper introduces tasks for Authorship Attribution, where different methods like MNB, SVM, BERT, ALBERT, and RoBERTa are used to attribute authorship to different LLMs involved in writing the collaborative stories . The study evaluates the performance of these methods based on the number of authors contributing to the story, ranging from 1 to 4 .

In summary, the paper introduces innovative approaches to collaborative story generation, authorship analysis, and attribution using LLMs, aiming to address the complexities of multi-authored text generation and establish benchmarks for analyzing authorship in collaborative writing scenarios . The paper "CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis" introduces several characteristics and advantages compared to previous methods in the field of collaborative story generation and authorship analysis using Large Language Models (LLMs) .

  1. Extension of Authorship Analysis Tasks: The study extends the PAN tasks for multi-LLM scenarios, focusing on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection, which have been persistent challenges in analyzing multi-authored text among humans for over 15 years . This extension aims to establish benchmarks for authorship-related tasks using LLMs, providing a more comprehensive approach to analyzing collaborative writing scenarios.

  2. Performance Comparison with Baseline Methods: The research compares the performance of different baseline methods such as Multinomial Naive Bayes, Support Vector Machine, BERT, ALBERT, and RoBERTa in predicting whether a story is written by multiple authors or not . The study evaluates the performance based on the number of authors involved in writing the story, ranging from 1 to 5, providing insights into the effectiveness of these methods in multi-authored text analysis.

  3. Fine-Tuning and Reporting Performance: The paper fine-tunes and reports the performance of the baseline methods using various descriptive features such as vocabulary richness, % of stopwords, readability, entropy, and coherence . This detailed analysis allows for a comprehensive evaluation of the effectiveness of different methods in handling multi-authored text scenarios.

  4. Addressing Nuanced Authorship Concerns: The study raises nuanced authorship concerns in the context of collaborative story generation using LLMs, questioning who should be considered the true creative source in such scenarios . It delves into questions of crediting all LLMs involved, acknowledging human developers, and determining ownership based on factors like word count, narrative depth, or plot twists, contributing to a deeper understanding of authorship in collaborative writing settings.

  5. Real-World Implications and Future Work: The research introduces authorship-related tasks using CollabStory to accurately discern the usage of multiple LLMs in texts, addressing concerns related to ownership and proving the origins of creative work . The extension of PAN-inspired authorship tasks is closely linked to real-world implications, paving the way for further exploration of collaborative writing scenarios and authorship attribution in the context of generative AI.

In summary, the paper's detailed analysis, extension of authorship tasks, performance comparison with baseline methods, and exploration of nuanced authorship concerns contribute significantly to advancing the understanding of collaborative story generation and authorship analysis using LLMs .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of collaborative story generation and authorship analysis. Noteworthy researchers in this area include Zhong et al. , who utilized "writing modes" as a control signal for better alignment during co-writing with humans, and CoAuthor, which positioned GPT3.5 as a writing collaborator for over 50 human participants . Additionally, the CollabStory dataset was developed by a team that includes authors such as Groeneveld, Mukherjee, and others .

The key to the solution mentioned in the paper revolves around the creation of a dataset called CollabStory, which simulates scenarios where different open-source Large Language Models (LLMs) collaboratively work on a storyline, passing control of the story from one LLM to the next. This dataset enables the study of collaborative creative story writing involving multiple LLMs, addressing challenges related to plagiarism detection, credit assignment, maintaining academic integrity, and copyright concerns . The dataset contains stories written by up to five LLMs, with a focus on machine-machine collaboration .


How were the experiments in the paper designed?

The experiments in the paper were designed to explore collaborative creative story-writing scenarios involving multiple Language Model Models (LLMs) . The study focused on single-author to multi-author scenarios, where up to five LLMs co-authored creative stories . The researchers aimed to address challenges related to plagiarism detection, credit assignment, maintaining academic integrity, and addressing copyright infringement concerns in the context of LLM-LLM collaboration . The dataset created for the experiments, called CollabStory, involved using five frequently used LLMs to simulate a scenario where LLMs from different organizations collaboratively work on a storyline . The stories in the dataset varied in the number of authors/LLMs involved, ranging from being written by a single LLM to collaboratively written by up to all five LLMs . The experiments aimed to demonstrate the potential of combining the expertise of LLMs specialized in various tasks for collaborative story generation and authorship analysis .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is called CollabStory, which is the first exclusively LLM-generated collaborative story dataset . The code for this dataset is open source and available at the following GitHub link: https://github.com/saranya-venkatraman/multi_llm_story_writing .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study focuses on collaborative creative story-writing scenarios involving multiple Language Model Models (LLMs) collaborating with each other . The dataset created, CollabStory, is the largest publicly available dataset of creative stories written collaboratively by different LLMs . The methodology involved generating stories using five open-source instruction-tuned LLMs, simulating a scenario where LLMs from different sources collaboratively work on a storyline . This approach aligns with the hypothesis that multi-LLM collaboration can lead to unique challenges and opportunities in creative writing tasks .

The study demonstrates the implications of Multi-LLM settings for various stakeholders, including LLM developers and end-users, highlighting considerations such as credit assignment and the legality of usage in the generative AI landscape . By replicating tasks from previous works and setting new baselines for authorship-related tasks in LLM-LLM collaboration, the paper addresses the emerging challenges posed by machine-machine collaboration . This analysis supports the hypothesis that current baselines are indeed challenged by the complexities of multi-LLM collaboration in text generation tasks .

Furthermore, the post-processing and filtering steps applied to the dataset ensure the quality and integrity of the generated stories, aligning with the hypothesis that maintaining academic integrity, addressing copyright concerns, and ensuring coherence in collaborative writing are crucial aspects to consider in multi-LLM settings . The descriptive statistics provided in the study offer insights into the differences between LLM-coauthored and human-written stories, supporting the hypothesis that collaborative writing with LLMs presents unique challenges that need to be addressed .

In conclusion, the experiments and results presented in the paper offer comprehensive support for the scientific hypotheses related to multi-LLM collaborative story generation and authorship analysis. The study's methodology, findings, and analyses contribute significantly to understanding the implications and challenges of machine-machine collaboration in creative writing tasks, validating the importance of exploring new techniques to navigate the complexities of multi-LLM settings in text generation .


What are the contributions of this paper?

The paper "CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis" makes several significant contributions:

  • It introduces the concept of Multi-LLM collaborative story-writing scenarios, where multiple Language Model Models (LLMs) collaborate to create coherent storylines .
  • The paper presents the CollabStory dataset, which is the largest publicly available dataset for creative stories collaboratively written by different LLMs, enabling the study of machine-machine collaboration in generating text .
  • It addresses the challenges and considerations involved in multi-LLM settings, such as credit assignment, legality of usage, and the implications for various stakeholders like LLM developers and end-users .
  • The study explores the potential of combining the expertise of LLMs specialized in various tasks, highlighting the emergence of LLMs conversing for continuous generative tasks in open domains .
  • The paper discusses the importance of developing methods to tackle the challenges posed by machine-machine collaboration, such as the potential misuse by malicious actors to spread misinformation .
  • It provides insights into the implications of LLM-LLM collaboration for addressing ongoing challenges related to plagiarism detection, credit assignment, maintaining academic integrity, and copyright concerns in educational settings .

What work can be continued in depth?

Further research in the field of collaborative story generation and authorship analysis can delve deeper into several aspects based on the provided context:

  • Exploration of Multi-LLM Settings: Future studies can explore the implications of Multi-LLM settings for different stakeholders, such as LLM developers and end-users, focusing on considerations like credit assignment and legality of usage .
  • Enhancing Collaborative Writing Scenarios: Research can focus on enhancing collaborative creative story-writing scenarios involving multiple LLMs, addressing challenges like plagiarism detection, credit assignment, and maintaining academic integrity .
  • Dataset Development: There is a need to develop more datasets that can be leveraged to understand collaborative story writing, similar to the STORIUM dataset, which contains creative stories written through human-human collaboration .
  • Ethical Considerations: Future work should also address ethical considerations related to using LLMs for creative story writing, including potential biases and harmful stereotypes present in the LLMs' original training data, transparency in content attribution, and guidelines to ensure LLMs enhance human creativity without undermining it .
  • Comparison with Existing Datasets: Researchers can further compare CollabStory with other existing collaborative creative story datasets to identify strengths, weaknesses, and areas for improvement in collaborative story generation tasks .
Tables
8
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