Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries

Adam Yang, Chen Chen, Konstantinos Pitas·May 22, 2024

Summary

This paper investigates the challenge of estimating uncertainty in closed-source large language models (LLMs) without built-in measures. The authors propose a method that uses rephrased queries to gauge model confidence, improving calibration through consistency across different question formulations. The study employs two rephrasing strategies (synonym substitution and verbose queries) and develops theoretical frameworks for top-1 and top-k decoding. It finds that rephrasing significantly enhances calibration, with AUROC scores increasing by 10-40%, ECE and TACE decreasing by 10-30%, and Brier scores reduced. The research highlights the effectiveness of rephrasing over noise-based approaches and suggests it as a simple yet powerful tool for enhancing LLM reliability, particularly in critical applications. It also evaluates various methods on multiple-choice tasks and datasets, demonstrating the potential for better accuracy and calibration through rephrasing.

Paper digest

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

The paper aims to address the issue of uncertainty estimation in closed-source language models, specifically focusing on the calibration of modern neural networks . This problem is not entirely new, as there is existing literature on estimating uncertainty in deep neural network models when access to the softmax categorical distribution is available . The paper delves into various methods such as rephrasing queries, leveraging multiple chains of thoughts, and deriving varied responses to enhance accuracy and provide well-calibrated uncertainty estimates . It also explores the challenges posed by closed-source language models where direct access to model logits is limited, requiring alternative approaches for uncertainty estimation .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that rephrasing queries in closed-source language models can primarily act to temper the probability of the most probable class, making the model less confident and potentially better calibrated . The study explores how rephrasing affects the distribution of probabilities for the most probable answer, aiming to improve the calibration of the model by reducing its confidence in predictions . The research investigates the impact of rephrasing on the uncertainty estimation in language models, particularly focusing on top-k decoding scenarios and the logistic distribution assumption for the noise in the latent space .


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

The paper "Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries" proposes several innovative ideas, methods, and models related to uncertainty estimation in closed-source language models . Here are some key points from the paper:

  1. Leveraging Multiple Chains of Thoughts: The paper by Wang et al. (2022) suggests using multiple chains of thoughts to derive varied responses, indicating that a majority vote across these answers can enhance accuracy and provide well-calibrated uncertainty estimates .

  2. Rephrasing Queries: The study explores the effectiveness of rephrasing queries to improve accuracy and calibration in closed-source language models. It compares different rephrasing methods such as "expansion" and "reword" that make queries more verbose or substitute words with synonyms, showing that these methods lead to better calibration gains .

  3. Top-k Decoding and Rephrasing: The paper evaluates the impact of top-k decoding with and without rephrasing, highlighting that while top-k decoding can improve calibration, it may result in a drop in accuracy. Rephrasing queries introduces stochasticity in answers, affecting calibration and accuracy trade-offs .

  4. Comparison with Chain-of-Thought (CoT): The study compares its uncertainty estimation method with Chain-of-Thought (CoT) by Wei et al. (2022) and finds competitive results with CoT. However, the proposed method is considered easier and more natural to implement for human interactions via text .

  5. Novel Uncertainty Quantification Metric: The paper introduces a novel uncertainty quantification metric by sampling multiple responses and using a BERT model to categorize these answers. This approach involves calculating the entropy of the empirical distribution, offering an alternative method for uncertainty estimation .

  6. Rephrasing for Improved Performance: Deng et al. (2023) and Zheng et al. (2023) are mentioned for their work on rephrasing queries to enhance model performance. Deng et al. demonstrated that expanding questions with supplementary details through a zero-shot prompt can significantly improve model performance, while Zheng et al. asked LLMs to derive high-level concepts before reasoning and answering questions, leading to performance boosts .

Overall, the paper introduces innovative approaches such as leveraging multiple chains of thoughts, rephrasing queries, and exploring novel uncertainty quantification metrics to enhance the accuracy and calibration of closed-source language models . The paper "Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries" introduces innovative characteristics and advantages compared to previous methods in uncertainty estimation in closed-source language models .

  1. Rephrasing Methods: The study explores various rephrasing methods such as "expansion" and "reword" that make queries more verbose or substitute words with synonyms. These methods exhibit different calibration gains, with "expansion" outperforming alternatives by 1-5% in AUROC and ≈ 0.05 in Brier score. The rephrasing techniques significantly improve calibration, especially for smaller models, by tempering the probability of the top class, leading to enhanced calibration metrics like ECE and AUROC .

  2. Performance Improvement: The paper demonstrates that rephrasing queries can outperform naive baselines by 10-40% in AUROC, 10-30% in ECE, and 0-0.4 in Brier. Compared to the "hint" based approach by Xiong et al. (2023), the proposed method typically achieves a 10-20% improvement in AUROC, 5-10% in ECE, and 0.1 in Brier. Notably, the rephrasing methods show significant accuracy gains, with the "expansion" and "reword" methods being particularly effective in enhancing calibration metrics .

  3. Ease of Implementation: In comparison with the Chain-of-Thought (CoT) method by Wei et al. (2022), the proposed approach in the paper yields competitive results with CoT while being significantly easier and more natural to implement for human interactions via text with a closed-source language model. The simplicity of the rephrasing process contrasts with the complexity of obtaining reasoning steps in CoT, making the proposed method more user-friendly and accessible for text-based interactions .

  4. Novel Uncertainty Quantification Metric: The paper introduces a novel uncertainty quantification metric that involves sampling multiple responses and utilizing a BERT model to categorize these answers. This approach offers an alternative method for uncertainty estimation, although it comes with the disadvantage of being computationally expensive and requiring access to a secondary LLM. Despite this drawback, the novel metric presents a unique perspective on uncertainty estimation in closed-source language models .

In summary, the paper's rephrasing methods, performance improvements, ease of implementation compared to CoT, and the introduction of a novel uncertainty quantification metric contribute to advancing uncertainty estimation in closed-source language models, offering enhanced calibration and accuracy in uncertainty predictions .


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 works exist in the field of estimating uncertainty in closed-source language models via multiple rephrased queries. Noteworthy researchers in this field include Kadavath et al. (2022), Pacchiardi et al. (2023), and Wei et al. (2022) . The key to the solution mentioned in the paper involves estimating the uncertainty of closed-source language models by asking the model multiple rephrased questions and using the similarity of the answers as an estimate of uncertainty. This method aims to improve the calibration of uncertainty estimates compared to the baseline and provides insights into designing optimal query strategies for test calibration .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the uncertainty estimation in closed-source language models through multiple rephrased queries. The experiments included top-k decoding with and without rephrasing, relaxed temperature sampling, and comparisons with other methods like nucleus sampling. The results showed that top-k decoding with rephrasing improved calibration but led to a drop in accuracy compared to top-1 decoding. Rephrasing was observed to temper the probability of the top class, enhancing calibration, especially for smaller models. The experiments also assessed the impact of different rephrasing methods on calibration gains, with methods like "expansion" and "reword" showing significant improvements in calibration metrics .


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

The dataset used for quantitative evaluation in the study is OpenBookQA . The code used in the study is not explicitly mentioned to be 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 strong support for the scientific hypotheses that need to be verified. The study explores uncertainty estimation in closed-source language models (LLMs) through multiple rephrased queries . The findings suggest that leveraging multiple chains of thoughts to derive varied responses enhances accuracy and yields well-calibrated uncertainty estimates . Additionally, the study indicates that rephrasing primarily acts to temper the probability of the top class, leading to improved calibration, especially for smaller models . The results show that a hyperparameter-optimized choice of rephrasing combined with top-1 decoding outperforms or matches all other method combinations in various metrics .

Moreover, the paper discusses related works in the field of estimating uncertainty in closed-source LLMs, highlighting the importance of verbalized confidence and the use of additional unrelated binary questions to check the accuracy of answers for well-calibrated uncertainty estimates . The comparison between rephrasing methods and white-box logit uncertainty estimation demonstrates that rephrasing methods achieve similar calibration to having access to last layer logits, as evidenced by metrics such as AUROC and Brier score . This comparison further supports the effectiveness of rephrasing in uncertainty estimation .

Overall, the experiments and results presented in the paper provide a comprehensive analysis of uncertainty estimation in closed-source language models, supporting the scientific hypotheses and contributing valuable insights to the field of natural language generation and uncertainty estimation .


What are the contributions of this paper?

The paper makes several contributions:

  • It provides insights into the labor market impact potential of large language models .
  • It explores hierarchical neural story generation .
  • It discusses the calibration of modern neural networks .
  • It delves into the curious case of neural text degeneration .
  • It surveys hallucination in large language models, addressing principles, taxonomy, challenges, and open questions .
  • It presents Mistral 7b, a research work .
  • It introduces Llama 2, focusing on open foundation and fine-tuned chat models .
  • It discusses Lora ensembles for large language model fine-tuning .
  • It explores self-consistency to improve chain of thought reasoning in language models .
  • It investigates lie detection in black-box language models by asking unrelated questions .

What work can be continued in depth?

Further research in the field of uncertainty estimation in closed-source language models can be expanded in several directions:

  • Exploring Multiple Rephrased Queries: Continuing to investigate the effectiveness of using multiple rephrased queries to estimate uncertainty in closed-source language models, as proposed by Yang et al. (2023) .
  • Comparative Studies: Conducting comparative studies with existing methods like Chain-of-Thought by Wei et al. (2022) to evaluate the performance and ease of implementation of different uncertainty estimation approaches .
  • Calibration Improvement: Researching methods to enhance the calibration of uncertainty estimates in closed-source language models, potentially by leveraging varied responses from multiple chains of thoughts as suggested by Wang et al. (2022) .
  • User Interaction: Exploring ways to streamline the process of inspecting and vetting LLM answers by users, especially for critical applications, to address the challenge of unreliable text generations .
  • Theoretical Frameworks: Developing theoretical frameworks to support the effectiveness of multiple rephrased queries in obtaining calibrated uncertainty estimates in closed-source language models .
  • Optimal Query Strategies: Investigating how query strategies should be designed for optimal test calibration in closed-source language models, building on the insights provided by recent research in the field .

Introduction
Background
Lack of native uncertainty measures in LLMs
Importance of reliable uncertainty estimates
Objective
To propose a rephrasing method for improved calibration
Enhance LLM reliability in critical applications
Method
Data Collection
Query Rephrasing Strategies
Synonym Substitution
Collection of synonymous queries
Verbose Queries
Development of detailed and varied questions
Data Preprocessing
Selection and preprocessing of rephrased queries
Alignment with original model responses
Top-1 and Top-k Decoding Theories
Formulation of theoretical frameworks
Evaluation of model confidence under different decoding strategies
Calibration Improvement
Calibration metrics: AUROC, ECE, TACE, and Brier scores
Quantitative analysis of rephrasing impact
Comparison with Noise-based Approaches
Evaluation of rephrasing effectiveness vs noise-based methods
Experiments and Evaluation
Multi-choice Tasks and Datasets
Selection of diverse datasets
Performance analysis on different tasks
Accuracy and calibration improvements with rephrasing
Case Studies
Real-world application scenarios
Demonstrating enhanced reliability in critical situations
Conclusion
Summary of key findings
Rephrasing as a practical tool for LLMs
Future research directions and implications
Limitations and Future Work
Addressing potential drawbacks of the method
Suggestions for further improvements and extensions
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What method does the paper propose to address uncertainty estimation in closed-source LLMs?
What is the primary focus of the paper?
What are the improvements in performance metrics reported by the research using rephrasing strategies?
How do rephrased queries contribute to model calibration, as per the study?

Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries

Adam Yang, Chen Chen, Konstantinos Pitas·May 22, 2024

Summary

This paper investigates the challenge of estimating uncertainty in closed-source large language models (LLMs) without built-in measures. The authors propose a method that uses rephrased queries to gauge model confidence, improving calibration through consistency across different question formulations. The study employs two rephrasing strategies (synonym substitution and verbose queries) and develops theoretical frameworks for top-1 and top-k decoding. It finds that rephrasing significantly enhances calibration, with AUROC scores increasing by 10-40%, ECE and TACE decreasing by 10-30%, and Brier scores reduced. The research highlights the effectiveness of rephrasing over noise-based approaches and suggests it as a simple yet powerful tool for enhancing LLM reliability, particularly in critical applications. It also evaluates various methods on multiple-choice tasks and datasets, demonstrating the potential for better accuracy and calibration through rephrasing.
Mind map
Evaluation of rephrasing effectiveness vs noise-based methods
Evaluation of model confidence under different decoding strategies
Formulation of theoretical frameworks
Development of detailed and varied questions
Verbose Queries
Collection of synonymous queries
Synonym Substitution
Demonstrating enhanced reliability in critical situations
Real-world application scenarios
Accuracy and calibration improvements with rephrasing
Performance analysis on different tasks
Selection of diverse datasets
Comparison with Noise-based Approaches
Top-1 and Top-k Decoding Theories
Query Rephrasing Strategies
Enhance LLM reliability in critical applications
To propose a rephrasing method for improved calibration
Importance of reliable uncertainty estimates
Lack of native uncertainty measures in LLMs
Suggestions for further improvements and extensions
Addressing potential drawbacks of the method
Future research directions and implications
Rephrasing as a practical tool for LLMs
Summary of key findings
Case Studies
Multi-choice Tasks and Datasets
Calibration Improvement
Data Preprocessing
Data Collection
Objective
Background
Limitations and Future Work
Conclusion
Experiments and Evaluation
Method
Introduction
Outline
Introduction
Background
Lack of native uncertainty measures in LLMs
Importance of reliable uncertainty estimates
Objective
To propose a rephrasing method for improved calibration
Enhance LLM reliability in critical applications
Method
Data Collection
Query Rephrasing Strategies
Synonym Substitution
Collection of synonymous queries
Verbose Queries
Development of detailed and varied questions
Data Preprocessing
Selection and preprocessing of rephrased queries
Alignment with original model responses
Top-1 and Top-k Decoding Theories
Formulation of theoretical frameworks
Evaluation of model confidence under different decoding strategies
Calibration Improvement
Calibration metrics: AUROC, ECE, TACE, and Brier scores
Quantitative analysis of rephrasing impact
Comparison with Noise-based Approaches
Evaluation of rephrasing effectiveness vs noise-based methods
Experiments and Evaluation
Multi-choice Tasks and Datasets
Selection of diverse datasets
Performance analysis on different tasks
Accuracy and calibration improvements with rephrasing
Case Studies
Real-world application scenarios
Demonstrating enhanced reliability in critical situations
Conclusion
Summary of key findings
Rephrasing as a practical tool for LLMs
Future research directions and implications
Limitations and Future Work
Addressing potential drawbacks of the method
Suggestions for further improvements and extensions

Paper digest

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

The paper aims to address the issue of uncertainty estimation in closed-source language models, specifically focusing on the calibration of modern neural networks . This problem is not entirely new, as there is existing literature on estimating uncertainty in deep neural network models when access to the softmax categorical distribution is available . The paper delves into various methods such as rephrasing queries, leveraging multiple chains of thoughts, and deriving varied responses to enhance accuracy and provide well-calibrated uncertainty estimates . It also explores the challenges posed by closed-source language models where direct access to model logits is limited, requiring alternative approaches for uncertainty estimation .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that rephrasing queries in closed-source language models can primarily act to temper the probability of the most probable class, making the model less confident and potentially better calibrated . The study explores how rephrasing affects the distribution of probabilities for the most probable answer, aiming to improve the calibration of the model by reducing its confidence in predictions . The research investigates the impact of rephrasing on the uncertainty estimation in language models, particularly focusing on top-k decoding scenarios and the logistic distribution assumption for the noise in the latent space .


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

The paper "Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries" proposes several innovative ideas, methods, and models related to uncertainty estimation in closed-source language models . Here are some key points from the paper:

  1. Leveraging Multiple Chains of Thoughts: The paper by Wang et al. (2022) suggests using multiple chains of thoughts to derive varied responses, indicating that a majority vote across these answers can enhance accuracy and provide well-calibrated uncertainty estimates .

  2. Rephrasing Queries: The study explores the effectiveness of rephrasing queries to improve accuracy and calibration in closed-source language models. It compares different rephrasing methods such as "expansion" and "reword" that make queries more verbose or substitute words with synonyms, showing that these methods lead to better calibration gains .

  3. Top-k Decoding and Rephrasing: The paper evaluates the impact of top-k decoding with and without rephrasing, highlighting that while top-k decoding can improve calibration, it may result in a drop in accuracy. Rephrasing queries introduces stochasticity in answers, affecting calibration and accuracy trade-offs .

  4. Comparison with Chain-of-Thought (CoT): The study compares its uncertainty estimation method with Chain-of-Thought (CoT) by Wei et al. (2022) and finds competitive results with CoT. However, the proposed method is considered easier and more natural to implement for human interactions via text .

  5. Novel Uncertainty Quantification Metric: The paper introduces a novel uncertainty quantification metric by sampling multiple responses and using a BERT model to categorize these answers. This approach involves calculating the entropy of the empirical distribution, offering an alternative method for uncertainty estimation .

  6. Rephrasing for Improved Performance: Deng et al. (2023) and Zheng et al. (2023) are mentioned for their work on rephrasing queries to enhance model performance. Deng et al. demonstrated that expanding questions with supplementary details through a zero-shot prompt can significantly improve model performance, while Zheng et al. asked LLMs to derive high-level concepts before reasoning and answering questions, leading to performance boosts .

Overall, the paper introduces innovative approaches such as leveraging multiple chains of thoughts, rephrasing queries, and exploring novel uncertainty quantification metrics to enhance the accuracy and calibration of closed-source language models . The paper "Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries" introduces innovative characteristics and advantages compared to previous methods in uncertainty estimation in closed-source language models .

  1. Rephrasing Methods: The study explores various rephrasing methods such as "expansion" and "reword" that make queries more verbose or substitute words with synonyms. These methods exhibit different calibration gains, with "expansion" outperforming alternatives by 1-5% in AUROC and ≈ 0.05 in Brier score. The rephrasing techniques significantly improve calibration, especially for smaller models, by tempering the probability of the top class, leading to enhanced calibration metrics like ECE and AUROC .

  2. Performance Improvement: The paper demonstrates that rephrasing queries can outperform naive baselines by 10-40% in AUROC, 10-30% in ECE, and 0-0.4 in Brier. Compared to the "hint" based approach by Xiong et al. (2023), the proposed method typically achieves a 10-20% improvement in AUROC, 5-10% in ECE, and 0.1 in Brier. Notably, the rephrasing methods show significant accuracy gains, with the "expansion" and "reword" methods being particularly effective in enhancing calibration metrics .

  3. Ease of Implementation: In comparison with the Chain-of-Thought (CoT) method by Wei et al. (2022), the proposed approach in the paper yields competitive results with CoT while being significantly easier and more natural to implement for human interactions via text with a closed-source language model. The simplicity of the rephrasing process contrasts with the complexity of obtaining reasoning steps in CoT, making the proposed method more user-friendly and accessible for text-based interactions .

  4. Novel Uncertainty Quantification Metric: The paper introduces a novel uncertainty quantification metric that involves sampling multiple responses and utilizing a BERT model to categorize these answers. This approach offers an alternative method for uncertainty estimation, although it comes with the disadvantage of being computationally expensive and requiring access to a secondary LLM. Despite this drawback, the novel metric presents a unique perspective on uncertainty estimation in closed-source language models .

In summary, the paper's rephrasing methods, performance improvements, ease of implementation compared to CoT, and the introduction of a novel uncertainty quantification metric contribute to advancing uncertainty estimation in closed-source language models, offering enhanced calibration and accuracy in uncertainty predictions .


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 works exist in the field of estimating uncertainty in closed-source language models via multiple rephrased queries. Noteworthy researchers in this field include Kadavath et al. (2022), Pacchiardi et al. (2023), and Wei et al. (2022) . The key to the solution mentioned in the paper involves estimating the uncertainty of closed-source language models by asking the model multiple rephrased questions and using the similarity of the answers as an estimate of uncertainty. This method aims to improve the calibration of uncertainty estimates compared to the baseline and provides insights into designing optimal query strategies for test calibration .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the uncertainty estimation in closed-source language models through multiple rephrased queries. The experiments included top-k decoding with and without rephrasing, relaxed temperature sampling, and comparisons with other methods like nucleus sampling. The results showed that top-k decoding with rephrasing improved calibration but led to a drop in accuracy compared to top-1 decoding. Rephrasing was observed to temper the probability of the top class, enhancing calibration, especially for smaller models. The experiments also assessed the impact of different rephrasing methods on calibration gains, with methods like "expansion" and "reword" showing significant improvements in calibration metrics .


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

The dataset used for quantitative evaluation in the study is OpenBookQA . The code used in the study is not explicitly mentioned to be 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 strong support for the scientific hypotheses that need to be verified. The study explores uncertainty estimation in closed-source language models (LLMs) through multiple rephrased queries . The findings suggest that leveraging multiple chains of thoughts to derive varied responses enhances accuracy and yields well-calibrated uncertainty estimates . Additionally, the study indicates that rephrasing primarily acts to temper the probability of the top class, leading to improved calibration, especially for smaller models . The results show that a hyperparameter-optimized choice of rephrasing combined with top-1 decoding outperforms or matches all other method combinations in various metrics .

Moreover, the paper discusses related works in the field of estimating uncertainty in closed-source LLMs, highlighting the importance of verbalized confidence and the use of additional unrelated binary questions to check the accuracy of answers for well-calibrated uncertainty estimates . The comparison between rephrasing methods and white-box logit uncertainty estimation demonstrates that rephrasing methods achieve similar calibration to having access to last layer logits, as evidenced by metrics such as AUROC and Brier score . This comparison further supports the effectiveness of rephrasing in uncertainty estimation .

Overall, the experiments and results presented in the paper provide a comprehensive analysis of uncertainty estimation in closed-source language models, supporting the scientific hypotheses and contributing valuable insights to the field of natural language generation and uncertainty estimation .


What are the contributions of this paper?

The paper makes several contributions:

  • It provides insights into the labor market impact potential of large language models .
  • It explores hierarchical neural story generation .
  • It discusses the calibration of modern neural networks .
  • It delves into the curious case of neural text degeneration .
  • It surveys hallucination in large language models, addressing principles, taxonomy, challenges, and open questions .
  • It presents Mistral 7b, a research work .
  • It introduces Llama 2, focusing on open foundation and fine-tuned chat models .
  • It discusses Lora ensembles for large language model fine-tuning .
  • It explores self-consistency to improve chain of thought reasoning in language models .
  • It investigates lie detection in black-box language models by asking unrelated questions .

What work can be continued in depth?

Further research in the field of uncertainty estimation in closed-source language models can be expanded in several directions:

  • Exploring Multiple Rephrased Queries: Continuing to investigate the effectiveness of using multiple rephrased queries to estimate uncertainty in closed-source language models, as proposed by Yang et al. (2023) .
  • Comparative Studies: Conducting comparative studies with existing methods like Chain-of-Thought by Wei et al. (2022) to evaluate the performance and ease of implementation of different uncertainty estimation approaches .
  • Calibration Improvement: Researching methods to enhance the calibration of uncertainty estimates in closed-source language models, potentially by leveraging varied responses from multiple chains of thoughts as suggested by Wang et al. (2022) .
  • User Interaction: Exploring ways to streamline the process of inspecting and vetting LLM answers by users, especially for critical applications, to address the challenge of unreliable text generations .
  • Theoretical Frameworks: Developing theoretical frameworks to support the effectiveness of multiple rephrased queries in obtaining calibrated uncertainty estimates in closed-source language models .
  • Optimal Query Strategies: Investigating how query strategies should be designed for optimal test calibration in closed-source language models, building on the insights provided by recent research in the field .
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