USDC: A Dataset of $\underline{U}$ser $\underline{S}$tance and $\underline{D}$ogmatism in Long $\underline{C}$onversations

Mounika Marreddy, Subba Reddy Oota, Venkata Charan Chinni, Manish Gupta, Lucie Flek·June 24, 2024

Summary

This paper presents the USDC dataset, a resource for studying user stance and dogmatism in lengthy conversations about capitalism and socialism on Reddit. The authors employ large language models like Mistral Large and GPT-4 for zero-shot, one-shot, and few-shot annotations, classifying stance on a five-point scale and opinion on a four-point scale. The dataset aims to enhance personalization and market research by capturing nuanced opinions and opinion shifts in conversation threads. The study uses 764 conversations from 22 subreddits, with annotations created through majority voting, to fine-tune and instruction-tune deployable models. Performance evaluations show moderate agreement between models and human annotations, with room for improvement, particularly in detecting dogmatism. The research contributes the USDC dataset, code, and models, addressing the need for context-aware user analysis in online discussions.

Key findings

17

Paper digest

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

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide additional details or context so I can assist you better.


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that large language models (LLMs) can effectively generate annotations for natural language processing (NLP) tasks, such as stance classification and dogmatism identification, comparable to human annotators . The study aims to demonstrate that LLMs can be utilized for generating annotations in zero-shot or few-shot task settings, as well as comparing the quality of annotations produced by different language models . The research focuses on leveraging LLMs to provide clear user-level posts and dogmatism data, which are valuable for modeling dynamic user representations in conversations .


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

The paper proposes several new ideas, methods, and models in the field of user stance and dogmatism analysis in long conversations:

  1. Stance Detection and Dogmatism Labels: The paper introduces a dataset that includes clear user-level posts and dogmatism data for modeling dynamic user representations. It provides annotations for NLP tasks using Large Language Models (LLMs) to detect stance in posts and label user dogmatism in conversations .

  2. Annotation Generation with Large Language Models: The study aligns with the growing literature suggesting that large language models can effectively perform labeling tasks in NLP. It explores LLM-based annotation generation in zero-shot or few-shot task settings, comparing pairs of language models to assess the quality of annotations generated for NLP tasks such as sentiment analysis and natural language inference .

  3. Finetuning and Instruction-tuning of Small Language Models (SLMs): For stance classification, each user post is treated as an independent sample, while for dogmatism classification, the entire user conversation is considered a single sample. The paper employs 4-bit quantization, LoRA technique, and finetuning with Supervised Finetuning Trainer (SFTT) for SLMs. Additionally, instruction-tuning of SLMs is conducted on user conversations with gold labels from the dataset, using prompts similar to those used for LLMs . The paper introduces several characteristics and advantages of its proposed methods compared to previous approaches in user stance and dogmatism analysis in long conversations:

  4. Dynamic User Representations: The paper focuses on modeling dynamic user representations by considering user-level posts and dogmatism data in conversations. This approach allows for a more nuanced understanding of user behavior and beliefs over the course of a conversation, compared to static representations used in previous methods.

  5. Utilization of Large Language Models (LLMs): The study leverages Large Language Models (LLMs) for generating annotations in NLP tasks. By utilizing LLMs, the paper demonstrates the effectiveness of zero-shot or few-shot learning settings for tasks like sentiment analysis and natural language inference. This approach showcases the scalability and efficiency of LLMs in generating high-quality annotations compared to traditional methods.

  6. Fine-tuning and Instruction-tuning of Small Language Models (SLMs): The paper employs techniques such as 4-bit quantization, LoRA technique, and finetuning with Supervised Finetuning Trainer (SFTT) for Small Language Models (SLMs). Additionally, instruction-tuning of SLMs is conducted on user conversations with gold labels from the dataset. This approach enhances the performance of SLMs in stance and dogmatism classification tasks, providing a more tailored and effective modeling strategy compared to previous methods.

  7. Model Comparison and Evaluation: The paper compares the performance of different language models, including LLMs and SLMs, in stance detection and dogmatism labeling tasks. By evaluating the quality of annotations generated by these models and analyzing their effectiveness in classification tasks, the study provides a comprehensive comparison of model performance and highlights the advantages of using specific models for different aspects of user analysis in conversations.

Overall, the characteristics and advantages of the proposed methods in the paper demonstrate a novel and effective approach to user stance and dogmatism analysis, showcasing improvements in dynamic user representations, annotation generation with LLMs, and fine-tuning strategies for SLMs compared to previous methods in the field.


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?

Could you please specify the topic or field you are referring to so I can provide you with more accurate information?


How were the experiments in the paper designed?

The experiments in the paper were designed to analyze user stance and dogmatism in long conversations using specific methodologies and techniques . The experiments involved classifying user posts into different stance categories such as 'Somewhat In Favor', 'Somewhat Against', and 'Stance Not Inferrable' based on the content and context of the posts . Additionally, the experiments included identifying the level of dogmatism in user comments by categorizing them as 'Open to Dialogue', 'Firm but Open', 'Deeply Rooted', or 'Flexible' . The design of the experiments focused on leveraging large language models for annotation generation and fine-tuning to understand user behaviors and opinions in online conversations . The experiments aimed to provide insights into user interactions and attitudes towards various topics by analyzing the content of Reddit submissions and comments .


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

The dataset used for quantitative evaluation in the study is the USDC dataset, which is a resource for analyzing user stance and dogmatism in lengthy conversations about capitalism and socialism on Reddit . The code associated with the dataset is open source, as the research contributes the USDC dataset, code, and models to address the need for context-aware user analysis in online discussions .


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 valuable support for the scientific hypotheses that needed verification. The study focused on analyzing dogmatism information for the top two authors due to budget constraints, which limited the evaluation to frequent posters only . Despite this limitation, the findings offer insights into the authors' stances and dogmatism levels, showcasing a range of positions from being open to dialogue to strongly against certain viewpoints . While the study may not have evaluated all authors extensively, the analysis of the selected authors still contributes significantly to understanding user stances and dogmatism in long conversations.


What are the contributions of this paper?

This paper makes several key contributions:

  • It introduces a dataset focusing on User Stance and Dogmatism in Long Conversations, providing annotations for stance labels such as 'Strongly Against', 'Somewhat In Favor', and 'Stance Not Inferrable' .
  • The paper addresses the limitations of previous studies by presenting Stance detection for posts and Dogmatism labels of users in conversations, considering the entire context while preserving submission IDs .
  • It explores the use of Large Language Models (LLMs) for generating annotations for Natural Language Processing (NLP) tasks, showcasing the potential of LLMs in labeling complex tasks .
  • The dataset offers clear user-level posts and dogmatism data, which are valuable for modeling dynamic user representations and understanding opinion fluctuations in user conversations .
  • The paper also delves into the finetuning and instruction-tuning of Small Language Models (SLMs) for Stance classification and Dogmatism identification, providing insights into the methodology used for these tasks .
  • Additionally, it contributes to the literature on instruction-finetuned language models, highlighting the importance of scaling instruction-finetuned language models for improved language understanding .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

Is there a specific type of work you are referring to, or do you need more information on a particular area?

Tables

2

Introduction
Background
[ ] Rise of online discussions on capitalism and socialism
[ ] Importance of understanding user stance and opinion shifts
Objective
[ ] To develop and analyze the USDC dataset
[ ] Enhance personalization and market research through nuanced opinions
[ ] Improve context-aware user analysis in online conversations
Dataset Collection and Annotation
Data Source
[ ] Reddit conversations from 22 subreddits on capitalism and socialism
Annotation Process
Large Language Model Assistance
Zero-shot Annotation
Employing Mistral Large and GPT-4
One-shot and Few-shot Annotation
Fine-tuning models with specific prompts
Stance Classification
Five-point scale for stance detection
Opinion Assessment
Four-point scale for opinion evaluation
Annotation Method
[ ] Majority voting for consensus
[ ] Human evaluation for model performance
Model Development and Evaluation
Model Fine-tuning
[ ] Using annotated conversations to fine-tune models
Instruction-tuning
[ ] Enhancing model understanding of nuanced opinions and shifts
Model Performance
[ ] Moderate agreement with human annotations
[ ] Challenges in detecting dogmatism
[ ] Areas for improvement identified
Contributions
USDC Dataset
[ ] Release of the dataset for research purposes
Code and Models
[ ] Open-source code and pre-trained models
[ ] Facilitating context-aware user analysis in online discourse
Conclusion
[ ] Significance of the USDC dataset for studying online conversations
[ ] Potential applications in personalization and market research
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What is the purpose of the USDC dataset in terms of personalization and market research?
What is the primary focus of the USDC dataset?
Which platforms does the dataset analyze conversations about capitalism and socialism?
How do large language models contribute to the annotation process in the study?

USDC: A Dataset of $\underline{U}$ser $\underline{S}$tance and $\underline{D}$ogmatism in Long $\underline{C}$onversations

Mounika Marreddy, Subba Reddy Oota, Venkata Charan Chinni, Manish Gupta, Lucie Flek·June 24, 2024

Summary

This paper presents the USDC dataset, a resource for studying user stance and dogmatism in lengthy conversations about capitalism and socialism on Reddit. The authors employ large language models like Mistral Large and GPT-4 for zero-shot, one-shot, and few-shot annotations, classifying stance on a five-point scale and opinion on a four-point scale. The dataset aims to enhance personalization and market research by capturing nuanced opinions and opinion shifts in conversation threads. The study uses 764 conversations from 22 subreddits, with annotations created through majority voting, to fine-tune and instruction-tune deployable models. Performance evaluations show moderate agreement between models and human annotations, with room for improvement, particularly in detecting dogmatism. The research contributes the USDC dataset, code, and models, addressing the need for context-aware user analysis in online discussions.
Mind map
Four-point scale for opinion evaluation
Opinion Assessment
Five-point scale for stance detection
Stance Classification
Fine-tuning models with specific prompts
One-shot and Few-shot Annotation
Employing Mistral Large and GPT-4
Zero-shot Annotation
Facilitating context-aware user analysis in online discourse
Open-source code and pre-trained models
Release of the dataset for research purposes
Areas for improvement identified
Challenges in detecting dogmatism
Moderate agreement with human annotations
Enhancing model understanding of nuanced opinions and shifts
Using annotated conversations to fine-tune models
Human evaluation for model performance
Majority voting for consensus
Large Language Model Assistance
Reddit conversations from 22 subreddits on capitalism and socialism
Improve context-aware user analysis in online conversations
Enhance personalization and market research through nuanced opinions
To develop and analyze the USDC dataset
Importance of understanding user stance and opinion shifts
Rise of online discussions on capitalism and socialism
Potential applications in personalization and market research
Significance of the USDC dataset for studying online conversations
Code and Models
USDC Dataset
Model Performance
Instruction-tuning
Model Fine-tuning
Annotation Method
Annotation Process
Data Source
Objective
Background
Conclusion
Contributions
Model Development and Evaluation
Dataset Collection and Annotation
Introduction
Outline
Introduction
Background
[ ] Rise of online discussions on capitalism and socialism
[ ] Importance of understanding user stance and opinion shifts
Objective
[ ] To develop and analyze the USDC dataset
[ ] Enhance personalization and market research through nuanced opinions
[ ] Improve context-aware user analysis in online conversations
Dataset Collection and Annotation
Data Source
[ ] Reddit conversations from 22 subreddits on capitalism and socialism
Annotation Process
Large Language Model Assistance
Zero-shot Annotation
Employing Mistral Large and GPT-4
One-shot and Few-shot Annotation
Fine-tuning models with specific prompts
Stance Classification
Five-point scale for stance detection
Opinion Assessment
Four-point scale for opinion evaluation
Annotation Method
[ ] Majority voting for consensus
[ ] Human evaluation for model performance
Model Development and Evaluation
Model Fine-tuning
[ ] Using annotated conversations to fine-tune models
Instruction-tuning
[ ] Enhancing model understanding of nuanced opinions and shifts
Model Performance
[ ] Moderate agreement with human annotations
[ ] Challenges in detecting dogmatism
[ ] Areas for improvement identified
Contributions
USDC Dataset
[ ] Release of the dataset for research purposes
Code and Models
[ ] Open-source code and pre-trained models
[ ] Facilitating context-aware user analysis in online discourse
Conclusion
[ ] Significance of the USDC dataset for studying online conversations
[ ] Potential applications in personalization and market research
Key findings
17

Paper digest

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

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide additional details or context so I can assist you better.


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the hypothesis that large language models (LLMs) can effectively generate annotations for natural language processing (NLP) tasks, such as stance classification and dogmatism identification, comparable to human annotators . The study aims to demonstrate that LLMs can be utilized for generating annotations in zero-shot or few-shot task settings, as well as comparing the quality of annotations produced by different language models . The research focuses on leveraging LLMs to provide clear user-level posts and dogmatism data, which are valuable for modeling dynamic user representations in conversations .


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

The paper proposes several new ideas, methods, and models in the field of user stance and dogmatism analysis in long conversations:

  1. Stance Detection and Dogmatism Labels: The paper introduces a dataset that includes clear user-level posts and dogmatism data for modeling dynamic user representations. It provides annotations for NLP tasks using Large Language Models (LLMs) to detect stance in posts and label user dogmatism in conversations .

  2. Annotation Generation with Large Language Models: The study aligns with the growing literature suggesting that large language models can effectively perform labeling tasks in NLP. It explores LLM-based annotation generation in zero-shot or few-shot task settings, comparing pairs of language models to assess the quality of annotations generated for NLP tasks such as sentiment analysis and natural language inference .

  3. Finetuning and Instruction-tuning of Small Language Models (SLMs): For stance classification, each user post is treated as an independent sample, while for dogmatism classification, the entire user conversation is considered a single sample. The paper employs 4-bit quantization, LoRA technique, and finetuning with Supervised Finetuning Trainer (SFTT) for SLMs. Additionally, instruction-tuning of SLMs is conducted on user conversations with gold labels from the dataset, using prompts similar to those used for LLMs . The paper introduces several characteristics and advantages of its proposed methods compared to previous approaches in user stance and dogmatism analysis in long conversations:

  4. Dynamic User Representations: The paper focuses on modeling dynamic user representations by considering user-level posts and dogmatism data in conversations. This approach allows for a more nuanced understanding of user behavior and beliefs over the course of a conversation, compared to static representations used in previous methods.

  5. Utilization of Large Language Models (LLMs): The study leverages Large Language Models (LLMs) for generating annotations in NLP tasks. By utilizing LLMs, the paper demonstrates the effectiveness of zero-shot or few-shot learning settings for tasks like sentiment analysis and natural language inference. This approach showcases the scalability and efficiency of LLMs in generating high-quality annotations compared to traditional methods.

  6. Fine-tuning and Instruction-tuning of Small Language Models (SLMs): The paper employs techniques such as 4-bit quantization, LoRA technique, and finetuning with Supervised Finetuning Trainer (SFTT) for Small Language Models (SLMs). Additionally, instruction-tuning of SLMs is conducted on user conversations with gold labels from the dataset. This approach enhances the performance of SLMs in stance and dogmatism classification tasks, providing a more tailored and effective modeling strategy compared to previous methods.

  7. Model Comparison and Evaluation: The paper compares the performance of different language models, including LLMs and SLMs, in stance detection and dogmatism labeling tasks. By evaluating the quality of annotations generated by these models and analyzing their effectiveness in classification tasks, the study provides a comprehensive comparison of model performance and highlights the advantages of using specific models for different aspects of user analysis in conversations.

Overall, the characteristics and advantages of the proposed methods in the paper demonstrate a novel and effective approach to user stance and dogmatism analysis, showcasing improvements in dynamic user representations, annotation generation with LLMs, and fine-tuning strategies for SLMs compared to previous methods in the field.


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?

Could you please specify the topic or field you are referring to so I can provide you with more accurate information?


How were the experiments in the paper designed?

The experiments in the paper were designed to analyze user stance and dogmatism in long conversations using specific methodologies and techniques . The experiments involved classifying user posts into different stance categories such as 'Somewhat In Favor', 'Somewhat Against', and 'Stance Not Inferrable' based on the content and context of the posts . Additionally, the experiments included identifying the level of dogmatism in user comments by categorizing them as 'Open to Dialogue', 'Firm but Open', 'Deeply Rooted', or 'Flexible' . The design of the experiments focused on leveraging large language models for annotation generation and fine-tuning to understand user behaviors and opinions in online conversations . The experiments aimed to provide insights into user interactions and attitudes towards various topics by analyzing the content of Reddit submissions and comments .


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

The dataset used for quantitative evaluation in the study is the USDC dataset, which is a resource for analyzing user stance and dogmatism in lengthy conversations about capitalism and socialism on Reddit . The code associated with the dataset is open source, as the research contributes the USDC dataset, code, and models to address the need for context-aware user analysis in online discussions .


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 valuable support for the scientific hypotheses that needed verification. The study focused on analyzing dogmatism information for the top two authors due to budget constraints, which limited the evaluation to frequent posters only . Despite this limitation, the findings offer insights into the authors' stances and dogmatism levels, showcasing a range of positions from being open to dialogue to strongly against certain viewpoints . While the study may not have evaluated all authors extensively, the analysis of the selected authors still contributes significantly to understanding user stances and dogmatism in long conversations.


What are the contributions of this paper?

This paper makes several key contributions:

  • It introduces a dataset focusing on User Stance and Dogmatism in Long Conversations, providing annotations for stance labels such as 'Strongly Against', 'Somewhat In Favor', and 'Stance Not Inferrable' .
  • The paper addresses the limitations of previous studies by presenting Stance detection for posts and Dogmatism labels of users in conversations, considering the entire context while preserving submission IDs .
  • It explores the use of Large Language Models (LLMs) for generating annotations for Natural Language Processing (NLP) tasks, showcasing the potential of LLMs in labeling complex tasks .
  • The dataset offers clear user-level posts and dogmatism data, which are valuable for modeling dynamic user representations and understanding opinion fluctuations in user conversations .
  • The paper also delves into the finetuning and instruction-tuning of Small Language Models (SLMs) for Stance classification and Dogmatism identification, providing insights into the methodology used for these tasks .
  • Additionally, it contributes to the literature on instruction-finetuned language models, highlighting the importance of scaling instruction-finetuned language models for improved language understanding .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

Is there a specific type of work you are referring to, or do you need more information on a particular area?

Tables
2
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