ElicitationGPT: Text Elicitation Mechanisms via Language Models

Yifan Wu, Jason Hartline·June 13, 2024

Summary

The paper presents ElicitationGPT, a method that develops domain-knowledge-free scoring rules for evaluating text forecasts using large language models. It addresses the limitations of supervised fine-tuning and reinforcement learning by designing scoring rules that align with human preferences in text evaluation. The study focuses on creating truthful reporting mechanisms for text responses, using a combination of NLP tasks and a dataset with prompts, ground truth, and clusters. It compares various scoring rules, such as quadratic and V-shaped, to peer review ratings, improving machine learning model training and reducing manipulation. The paper contributes a framework for constructing scoring rules in peer grading systems, where textual feedback is found to be more aligned with human judgment than numerical ratings. It explores the use of simplifying assumptions and demonstrates the potential of text-based scoring rules for enhancing accuracy and effectiveness in large courses. ElicitationGPT is applied to student submissions, using LLMs for tasks like peer grading, and its properness is derived from the underlying scoring rules. The research evaluates different scoring mechanisms, including mean elicitation with proper scoring rules, and introduces adaptations for multi-dimensional settings and know-it-or-not indicators. ElicitationGPT is implemented using GPT-like models, with a focus on summarization, question answering, and a know-it-or-not scoring rule. The system is tested on peer review datasets, showing improved alignment with instructor scores and student grades compared to alternative methods. The study also highlights a vulnerability in AI models like GPT, where system-level instructions can manipulate evaluations. However, ElicitationGPT is designed to resist such manipulation, making it a more reliable assessment tool. The collection of papers in the study covers a wide range of topics, from question answering to mechanism design, contributing to the ongoing discussion on the use and improvement of language models in various applications.

Paper digest

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

The paper aims to address the issue of aligning scoring mechanisms with human preferences in text evaluation . Specifically, it focuses on designing proper scoring rules for text to evaluate responses against "ground truth" responses and assess their alignment with human evaluators . This problem is not entirely new, as it builds on existing work in the field of scoring rules and loss functions for numerical predictions . The paper extends this concept to the evaluation of text responses, emphasizing the importance of proper scoring rules in training machine learning models and ensuring alignment with human preferences .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the alignment of proper scoring rules for text with human preferences . The study focuses on constructing proper scoring rules for text and evaluating their alignment with human evaluators . The main goal is to assess how well these scoring rules rank responses in alignment with human rankings, ensuring that the scoring rules are proper and optimized for expected score relative to beliefs . The research explores the application of proper scoring rules in training machine learning models, emphasizing the importance of alignment with human preferences and the optimization of scoring rules for text .


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

The paper proposes novel ideas, methods, and models related to scoring rules for text alignment with human preferences and evaluations . It introduces proper scoring rules designed specifically for text to assess their alignment with human preferences . The paper contrasts the standard supervised fine-tuning (SFT) method, which evaluates predictions based on word sequences rather than semantic meaning, leading to misalignment with human preferences . In addressing this issue, the paper explores reinforcement learning from human feedback (RLHF) as a solution to improve alignment with human preferences . However, RLHF is noted to be vulnerable to manipulations . The proposed proper scoring rules for text aim to enhance alignment in SFT and mitigate manipulations in RLHF, offering a potential improvement in aligning text scoring with human preferences . The paper "ElicitationGPT: Text Elicitation Mechanisms via Language Models" introduces novel mechanisms for text elicitation using language models, specifically focusing on scoring rules for text alignment with human preferences and evaluations . One key characteristic of the proposed approach is the use of proper scoring rules tailored for text to assess alignment with human preferences, contrasting with standard supervised fine-tuning (SFT) methods that may lead to misalignment with human preferences due to the evaluation based on word sequences rather than semantic meaning . The paper explores reinforcement learning from human feedback (RLHF) as a solution to enhance alignment with human preferences, although noting vulnerabilities to manipulations .

Compared to previous methods, the ElicitationGPT approach offers several advantages. Firstly, ElicitationGPT is designed to be domain knowledge-free and requires basic oracle functionalities, making its performance more robust compared to direct GPT queries, which are susceptible to manipulations . Additionally, ElicitationGPT emphasizes properness, which is crucial in ensuring the alignment of text scoring with human preferences . The paper highlights that ElicitationGPT scores are less noisy than instructor scores, indicating a higher level of robustness in assessing peer reviews . Moreover, ElicitationGPT demonstrates better alignment with overall student grades, suggesting that textual reviews convey more information about students' true performance compared to numerical reviews .

Furthermore, the development of scoring rules for text in ElicitationGPT is essential for scaling large courses via peer grading without increasing the grading workload of instructors . By focusing on grading written feedback in peer reviews rather than numerical scores, ElicitationGPT places emphasis on providing constructive feedback, which is beneficial for learning outcomes and potentially more accurate in assessment . The paper underscores that the scoring rules for text have the potential to emphasize the right activities in peer reviews and improve accuracy in assessing submissions, contributing to the scalability of peer grading in educational settings .


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 text elicitation mechanisms via language models. Noteworthy researchers in this area include Li, Hartline, Shan, and Wu [2022], who optimized scoring rules for binary effort in peer grading scenarios, and Hartline, Shan, Li, and Wu [2023], who extended the model to include multi-dimensional effort optimization for scoring rules. Additionally, Gao et al. [2023] and Schneider et al. [2023] explored the use of language models for grading textual responses of students, focusing on comparing student answers to ground truth using different approaches .

The key to the solution mentioned in the paper involves constructing a multi-dimensional scoring rule based on an analysis of instructor reviews of similar questions (submissions of the same assignment). This scoring rule is then used to evaluate a student's answer (peer review) across various dimensions, leading to favorable results .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate proper scoring rules for alignment with human preferences . The empirical evaluation involved testing different configurations of ElicitationGPT on several datasets and comparing them to various benchmarks . The experiments included using peer review data from classes, such as algorithms and mechanism design, where student submissions were graded by their peers, and instructor scores were available . Additionally, the experiments aimed to assess the alignment of the proposed scoring rules with human evaluators by comparing them to manual instructor scores for the peer reviews .


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

The dataset used for quantitative evaluation consists of peer review data from three classes: two instances of an algorithms class (an undergraduate course) and one mechanism design class (a graduate course) . The code for ElicitationGPT is not explicitly mentioned as open source in the provided context.


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 substantial support for the scientific hypotheses that require verification. The paper focuses on designing proper scoring rules for text and evaluating their alignment with human preferences . The empirical evaluation conducted on peer reviews from a peer-grading dataset demonstrates a high degree of alignment between the textual scoring rules applied to the peer reviews and the ground truth reviews given by instructors . This alignment indicates that the scoring rules for text are better aligned with human preferences compared to traditional numeric scoring rules .

Moreover, the paper evaluates the proposed scoring rules on a dataset containing textual and numeric peer reviews, instructor reviews, and overall student grades . The analysis shows that the text scoring rules are more aligned with the overall student grades than the instructor's scores, indicating the effectiveness of the text scoring rules in evaluating peer reviews . Additionally, the paper discusses the limitations of existing methods such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) and proposes proper scoring rules for text as a potential solution to improve alignment with human preferences and avoid manipulations .

Overall, the experiments and results presented in the paper offer strong empirical evidence supporting the effectiveness of the proposed scoring rules for text in aligning with human preferences and evaluating peer reviews accurately .


What are the contributions of this paper?

TheTo provide a more accurate answer, could you please specify which paper you are referring to?


What work can be continued in depth?

Further research in the field can focus on the optimization of scoring rules for text, especially in the context of peer grading applications. Previous work has shown the importance of developing scoring rules for text to emphasize providing good written feedback in peer reviews, which can lead to better learning outcomes and potentially more accurate assessments compared to numerical grading tasks . This area of study is critical for scaling large courses through peer grading without increasing the instructor's grading workload . Additionally, exploring the robustness and reliability of ElicitationGPT in aligning with overall student grades compared to instructor scores can be a valuable avenue for future investigation .


Introduction
Background
Limitations of supervised fine-tuning and reinforcement learning in text evaluation
Importance of truthful reporting in text responses
Objective
Development of scoring rules for aligning with human preferences in text evaluation
Creation of a truthful reporting mechanism for text forecasts
Method
Data Collection
Dataset creation: prompts, ground truth, and clusters for NLP tasks
Peer review dataset for evaluation
Data Preprocessing
Textual feedback analysis
Simplifying assumptions for scoring rule construction
Scoring Rules
Quadratic and V-shaped scoring rules
Comparison with peer review ratings
Properness and resistance to manipulation
Multi-dimensional Scoring Mechanisms
Mean elicitation with proper scoring rules
Know-it-or-not indicators adaptation
Model Implementation
GPT-like models for summarization, question answering, and know-it-or-not scoring
Integration with LLMs for peer grading
Evaluation
Testing on peer review datasets
Alignment with instructor and student grades
Comparison with alternative methods
Vulnerabilities and Resilience
AI model manipulation risks
ElicitationGPT's resistance to manipulation
Applications and Framework
Framework for constructing scoring rules in peer grading systems
Textual feedback's alignment with human judgment
Contributions
Enhancing accuracy and effectiveness in large courses
Ongoing discussion on language models in mechanism design and applications
Conclusion
Summary of findings and implications for future research
Limitations and directions for further development of ElicitationGPT
Basic info
papers
machine learning
computer science and game theory
artificial intelligence
Advanced features
Insights
How does ElicitationGPT address the limitations of supervised fine-tuning and reinforcement learning in text forecast evaluation?
What is the focus of the study regarding truthful reporting mechanisms for text responses?
How does ElicitationGPT compare different scoring rules to peer review ratings, and what is its potential impact on machine learning model training?
What is the primary purpose of ElicitationGPT presented in the paper?

ElicitationGPT: Text Elicitation Mechanisms via Language Models

Yifan Wu, Jason Hartline·June 13, 2024

Summary

The paper presents ElicitationGPT, a method that develops domain-knowledge-free scoring rules for evaluating text forecasts using large language models. It addresses the limitations of supervised fine-tuning and reinforcement learning by designing scoring rules that align with human preferences in text evaluation. The study focuses on creating truthful reporting mechanisms for text responses, using a combination of NLP tasks and a dataset with prompts, ground truth, and clusters. It compares various scoring rules, such as quadratic and V-shaped, to peer review ratings, improving machine learning model training and reducing manipulation. The paper contributes a framework for constructing scoring rules in peer grading systems, where textual feedback is found to be more aligned with human judgment than numerical ratings. It explores the use of simplifying assumptions and demonstrates the potential of text-based scoring rules for enhancing accuracy and effectiveness in large courses. ElicitationGPT is applied to student submissions, using LLMs for tasks like peer grading, and its properness is derived from the underlying scoring rules. The research evaluates different scoring mechanisms, including mean elicitation with proper scoring rules, and introduces adaptations for multi-dimensional settings and know-it-or-not indicators. ElicitationGPT is implemented using GPT-like models, with a focus on summarization, question answering, and a know-it-or-not scoring rule. The system is tested on peer review datasets, showing improved alignment with instructor scores and student grades compared to alternative methods. The study also highlights a vulnerability in AI models like GPT, where system-level instructions can manipulate evaluations. However, ElicitationGPT is designed to resist such manipulation, making it a more reliable assessment tool. The collection of papers in the study covers a wide range of topics, from question answering to mechanism design, contributing to the ongoing discussion on the use and improvement of language models in various applications.
Mind map
Integration with LLMs for peer grading
GPT-like models for summarization, question answering, and know-it-or-not scoring
Know-it-or-not indicators adaptation
Mean elicitation with proper scoring rules
Properness and resistance to manipulation
Comparison with peer review ratings
Quadratic and V-shaped scoring rules
Comparison with alternative methods
Alignment with instructor and student grades
Testing on peer review datasets
Model Implementation
Multi-dimensional Scoring Mechanisms
Scoring Rules
Peer review dataset for evaluation
Dataset creation: prompts, ground truth, and clusters for NLP tasks
Creation of a truthful reporting mechanism for text forecasts
Development of scoring rules for aligning with human preferences in text evaluation
Importance of truthful reporting in text responses
Limitations of supervised fine-tuning and reinforcement learning in text evaluation
Limitations and directions for further development of ElicitationGPT
Summary of findings and implications for future research
Ongoing discussion on language models in mechanism design and applications
Enhancing accuracy and effectiveness in large courses
Textual feedback's alignment with human judgment
Framework for constructing scoring rules in peer grading systems
ElicitationGPT's resistance to manipulation
AI model manipulation risks
Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Contributions
Applications and Framework
Vulnerabilities and Resilience
Method
Introduction
Outline
Introduction
Background
Limitations of supervised fine-tuning and reinforcement learning in text evaluation
Importance of truthful reporting in text responses
Objective
Development of scoring rules for aligning with human preferences in text evaluation
Creation of a truthful reporting mechanism for text forecasts
Method
Data Collection
Dataset creation: prompts, ground truth, and clusters for NLP tasks
Peer review dataset for evaluation
Data Preprocessing
Textual feedback analysis
Simplifying assumptions for scoring rule construction
Scoring Rules
Quadratic and V-shaped scoring rules
Comparison with peer review ratings
Properness and resistance to manipulation
Multi-dimensional Scoring Mechanisms
Mean elicitation with proper scoring rules
Know-it-or-not indicators adaptation
Model Implementation
GPT-like models for summarization, question answering, and know-it-or-not scoring
Integration with LLMs for peer grading
Evaluation
Testing on peer review datasets
Alignment with instructor and student grades
Comparison with alternative methods
Vulnerabilities and Resilience
AI model manipulation risks
ElicitationGPT's resistance to manipulation
Applications and Framework
Framework for constructing scoring rules in peer grading systems
Textual feedback's alignment with human judgment
Contributions
Enhancing accuracy and effectiveness in large courses
Ongoing discussion on language models in mechanism design and applications
Conclusion
Summary of findings and implications for future research
Limitations and directions for further development of ElicitationGPT

Paper digest

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

The paper aims to address the issue of aligning scoring mechanisms with human preferences in text evaluation . Specifically, it focuses on designing proper scoring rules for text to evaluate responses against "ground truth" responses and assess their alignment with human evaluators . This problem is not entirely new, as it builds on existing work in the field of scoring rules and loss functions for numerical predictions . The paper extends this concept to the evaluation of text responses, emphasizing the importance of proper scoring rules in training machine learning models and ensuring alignment with human preferences .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the alignment of proper scoring rules for text with human preferences . The study focuses on constructing proper scoring rules for text and evaluating their alignment with human evaluators . The main goal is to assess how well these scoring rules rank responses in alignment with human rankings, ensuring that the scoring rules are proper and optimized for expected score relative to beliefs . The research explores the application of proper scoring rules in training machine learning models, emphasizing the importance of alignment with human preferences and the optimization of scoring rules for text .


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

The paper proposes novel ideas, methods, and models related to scoring rules for text alignment with human preferences and evaluations . It introduces proper scoring rules designed specifically for text to assess their alignment with human preferences . The paper contrasts the standard supervised fine-tuning (SFT) method, which evaluates predictions based on word sequences rather than semantic meaning, leading to misalignment with human preferences . In addressing this issue, the paper explores reinforcement learning from human feedback (RLHF) as a solution to improve alignment with human preferences . However, RLHF is noted to be vulnerable to manipulations . The proposed proper scoring rules for text aim to enhance alignment in SFT and mitigate manipulations in RLHF, offering a potential improvement in aligning text scoring with human preferences . The paper "ElicitationGPT: Text Elicitation Mechanisms via Language Models" introduces novel mechanisms for text elicitation using language models, specifically focusing on scoring rules for text alignment with human preferences and evaluations . One key characteristic of the proposed approach is the use of proper scoring rules tailored for text to assess alignment with human preferences, contrasting with standard supervised fine-tuning (SFT) methods that may lead to misalignment with human preferences due to the evaluation based on word sequences rather than semantic meaning . The paper explores reinforcement learning from human feedback (RLHF) as a solution to enhance alignment with human preferences, although noting vulnerabilities to manipulations .

Compared to previous methods, the ElicitationGPT approach offers several advantages. Firstly, ElicitationGPT is designed to be domain knowledge-free and requires basic oracle functionalities, making its performance more robust compared to direct GPT queries, which are susceptible to manipulations . Additionally, ElicitationGPT emphasizes properness, which is crucial in ensuring the alignment of text scoring with human preferences . The paper highlights that ElicitationGPT scores are less noisy than instructor scores, indicating a higher level of robustness in assessing peer reviews . Moreover, ElicitationGPT demonstrates better alignment with overall student grades, suggesting that textual reviews convey more information about students' true performance compared to numerical reviews .

Furthermore, the development of scoring rules for text in ElicitationGPT is essential for scaling large courses via peer grading without increasing the grading workload of instructors . By focusing on grading written feedback in peer reviews rather than numerical scores, ElicitationGPT places emphasis on providing constructive feedback, which is beneficial for learning outcomes and potentially more accurate in assessment . The paper underscores that the scoring rules for text have the potential to emphasize the right activities in peer reviews and improve accuracy in assessing submissions, contributing to the scalability of peer grading in educational settings .


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 text elicitation mechanisms via language models. Noteworthy researchers in this area include Li, Hartline, Shan, and Wu [2022], who optimized scoring rules for binary effort in peer grading scenarios, and Hartline, Shan, Li, and Wu [2023], who extended the model to include multi-dimensional effort optimization for scoring rules. Additionally, Gao et al. [2023] and Schneider et al. [2023] explored the use of language models for grading textual responses of students, focusing on comparing student answers to ground truth using different approaches .

The key to the solution mentioned in the paper involves constructing a multi-dimensional scoring rule based on an analysis of instructor reviews of similar questions (submissions of the same assignment). This scoring rule is then used to evaluate a student's answer (peer review) across various dimensions, leading to favorable results .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate proper scoring rules for alignment with human preferences . The empirical evaluation involved testing different configurations of ElicitationGPT on several datasets and comparing them to various benchmarks . The experiments included using peer review data from classes, such as algorithms and mechanism design, where student submissions were graded by their peers, and instructor scores were available . Additionally, the experiments aimed to assess the alignment of the proposed scoring rules with human evaluators by comparing them to manual instructor scores for the peer reviews .


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

The dataset used for quantitative evaluation consists of peer review data from three classes: two instances of an algorithms class (an undergraduate course) and one mechanism design class (a graduate course) . The code for ElicitationGPT is not explicitly mentioned as open source in the provided context.


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 substantial support for the scientific hypotheses that require verification. The paper focuses on designing proper scoring rules for text and evaluating their alignment with human preferences . The empirical evaluation conducted on peer reviews from a peer-grading dataset demonstrates a high degree of alignment between the textual scoring rules applied to the peer reviews and the ground truth reviews given by instructors . This alignment indicates that the scoring rules for text are better aligned with human preferences compared to traditional numeric scoring rules .

Moreover, the paper evaluates the proposed scoring rules on a dataset containing textual and numeric peer reviews, instructor reviews, and overall student grades . The analysis shows that the text scoring rules are more aligned with the overall student grades than the instructor's scores, indicating the effectiveness of the text scoring rules in evaluating peer reviews . Additionally, the paper discusses the limitations of existing methods such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) and proposes proper scoring rules for text as a potential solution to improve alignment with human preferences and avoid manipulations .

Overall, the experiments and results presented in the paper offer strong empirical evidence supporting the effectiveness of the proposed scoring rules for text in aligning with human preferences and evaluating peer reviews accurately .


What are the contributions of this paper?

TheTo provide a more accurate answer, could you please specify which paper you are referring to?


What work can be continued in depth?

Further research in the field can focus on the optimization of scoring rules for text, especially in the context of peer grading applications. Previous work has shown the importance of developing scoring rules for text to emphasize providing good written feedback in peer reviews, which can lead to better learning outcomes and potentially more accurate assessments compared to numerical grading tasks . This area of study is critical for scaling large courses through peer grading without increasing the instructor's grading workload . Additionally, exploring the robustness and reliability of ElicitationGPT in aligning with overall student grades compared to instructor scores can be a valuable avenue for future investigation .

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