Towards Minimal Targeted Updates of Language Models with Targeted Negative Training

Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi·June 19, 2024

Summary

This collection of research papers explores various techniques for improving language models, with a focus on targeted negative training (TNT) to reduce unwanted outputs like hallucinations, toxicity, and disfluencies. TNT outperforms existing methods in balancing reduction without significantly altering the model's original behavior. The studies cover topics such as finetuning strategies, optimizing token-level constraints, and addressing bias, with experiments on datasets like XSUM, Civil Comments, and PaLM-2. TNT methods, like TNFF and TNRR, are shown to be effective in reducing unwanted content while maintaining fluency and similarity to the original model. The research also highlights the importance of iterative refinement, controlled generation, and the trade-offs between reducing unwanted behavior and preserving model performance, especially in low-resource scenarios. Some studies address specific issues like disfluency detection and mitigation, and the broader impact of these methods emphasizes the need for more sophisticated definitions of unwanted content in practical applications.

Key findings

12

Paper digest

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

The paper aims to address the challenge of updating language models to avoid generating undesirable outputs while minimally altering the model's behavior, which is referred to as a minimal targeted update . This problem is not entirely new, as existing strategies like retraining and finetuning have been used to modify models to address specific issues, but they can lead to the emergence of new problems . The proposed approach in the paper, called Targeted Negative Training (TNT), offers a method for making more targeted changes to a model by using negative examples from the model's generations to achieve minimal targeted updates .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to updating language models to avoid generating undesirable outputs while minimally altering the model's behavior, a concept referred to as a minimal targeted update . The research focuses on proposing a method called Targeted Negative Training (TNT) that utilizes negative examples from a model's generations to achieve updates that maintain the new distribution close to the original, unlike existing methods that only push down probability without controlling the updated distribution . The study demonstrates that TNT offers a better balance between reducing unwanted behavior and preserving the model's generation capabilities compared to baseline approaches, paving the way for a modeling paradigm based on iterative training updates that restrict models from producing undesirable outputs while retaining their impressive capabilities .


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

The paper "Towards Minimal Targeted Updates of Language Models with Targeted Negative Training" proposes a method called Targeted Negative Training (TNT) to achieve minimal targeted updates of language models . This method aims to address the challenge of updating a model to avoid undesirable outputs while minimally changing the model's behavior in other aspects . TNT uses negative examples from a model's generations to achieve updates that keep the new distribution close to the original, unlike existing losses for negative signal that only push down probability without controlling the updated distribution .

The proposed TNT method in the paper focuses on training-time strategies to improve a model's generations by minimizing unwanted behavior while preserving the model's impressive capabilities . This approach contrasts with existing techniques that introduce latency or complexity during prediction by pushing all desired model changes to inference time . The paper emphasizes the importance of maintaining the ease of use and speed of language models during prediction, making training-time strategies like TNT crucial for enhancing model generations .

Furthermore, the paper discusses the challenges in detoxifying language models and the need for strategies that can effectively control the generations of existing language models to avoid undesirable outputs . By introducing TNT as a method for minimal targeted updates, the paper contributes to the development of a modeling paradigm based on iterative training updates that constrain models from generating unwanted outputs while preserving their capabilities . The paper "Towards Minimal Targeted Updates of Language Models with Targeted Negative Training" introduces Targeted Negative Training (TNT) as a method to achieve minimal targeted updates of language models . Compared to previous methods, TNT offers several key characteristics and advantages:

  1. Improved Trade-off Between Similarity and Reduction: TNT methods provide a better trade-off between similarity to the original generations and reduction of unwanted behavior compared to baseline methods, especially up to a 50% reduction rate . This indicates that TNT methods excel in balancing the need to reduce unwanted behavior while maintaining similarity to the original model's generations.

  2. Effective Reduction of Unwanted Behavior: TNT methods, such as TNFF, TNRR, and TNRF, demonstrate superior performance in reducing unwanted behavior, particularly in toxicity reduction tasks, where they outperform baseline methods across all levels of toxicity rate reduction . This highlights the effectiveness of TNT in achieving targeted updates to minimize undesirable outputs.

  3. Minimization of Introduced Disfluencies: Even when achieving comparable similarity and reduction rates as baselines, TNT methods are significantly better at avoiding the introduction of new disfluencies in model generations . This indicates that TNT methods prioritize maintaining the quality of model outputs by minimizing the introduction of obvious disfluencies.

  4. Training-time Strategies for Model Improvement: Unlike techniques that introduce latency or complexity during prediction by pushing desired changes to inference time, TNT focuses on training-time strategies to enhance model generations . This approach ensures that model updates are integrated seamlessly without compromising the ease of use and speed of language models during prediction.

  5. Iterative Refinement of Models: TNT offers a means to iteratively refine a model after its initial training, contributing to the safety and reliability of autoregressive generative models . By targeting updates to avoid unwanted behavior in a targeted fashion, TNT enables continuous improvement of model behavior without the need for complete model retraining.

  6. Consideration of Model Size and Data Volume: TNT methods show promise in maintaining model behavior even with less data, suggesting practical efficacy for minimal targeted updates in low-data regimes . Additionally, TNT methods demonstrate a better trade-off between similarity and reduction as dataset size decreases, highlighting their adaptability to varying data volumes.

In conclusion, the characteristics and advantages of Targeted Negative Training (TNT) outlined in the paper emphasize its effectiveness in achieving minimal targeted updates of language models by balancing the reduction of unwanted behavior with the preservation of model capabilities and generation quality.


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 language models and text generation. Noteworthy researchers in this area include Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, Po-Sen Huang, Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, Yejin Choi, and many others .

The key to the solution mentioned in the paper "Towards Minimal Targeted Updates of Language Models with Targeted Negative Training" involves a finetuning procedure that only requires training one model using analytically computable token-level divergences. This approach focuses on pointwise constraints and aims to optimize analytical token-level divergences even with sequence-level annotations, providing a simpler algorithm compared to existing methods .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of different methods for minimal targeted updates of language models. The experiments involved comparing various techniques to reduce hallucination and toxicity rates in text generation while maintaining similarity to the original generations . These methods included NL+LL, UL+LL, TNRLL, TNFLL, and TNRLL, each with different parameters and objectives aimed at minimizing unwanted behaviors in generated text . The experiments also assessed the impact of dataset size on the effectiveness of the targeted update methods, showing that some methods performed better with less data, highlighting their practical efficacy in low-data regimes . The paper's experimental design focused on analyzing the trade-off between reducing unwanted behaviors like hallucination and toxicity while preserving the original model's behavior and minimizing obvious disfluencies in the generated text .


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

The dataset used for quantitative evaluation in the study is the Civil Comments dataset of online comments . The code used for determining whether a hallucination exists in the generated summary is open source and can be found in the code from Nan et al. (2021) .


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 "Towards Minimal Targeted Updates of Language Models with Targeted Negative Training" provide strong support for the scientific hypotheses that needed verification. The study introduces the concept of a minimal targeted update and proposes Targeted Negative Training (TNT) as a method to achieve such updates by using negative examples from a model's generations . The results demonstrate that TNT yields a better trade-off between reducing unwanted behavior, such as hallucinations and toxicity, while maintaining the model's generation behavior compared to baseline methods . This indicates that the proposed TNT method effectively minimizes unwanted outputs while preserving the impressive capabilities of language models, aligning with the scientific hypotheses of achieving minimal targeted updates .

Furthermore, the experiments conducted in the study show that TNT methods outperform baseline methods up to a 50% reduction rate in terms of similarity vs. reduction, highlighting the effectiveness of TNT in achieving the desired balance . The results also indicate that TNT methods struggle to reduce the hallucination rate beyond a certain point, but baseline methods achieve this at the expense of increasing obvious disfluencies . This analysis supports the hypothesis that TNT strikes a better balance between reducing unwanted behavior and maintaining model generation behavior, emphasizing the significance of the proposed method in achieving minimal targeted updates .

In conclusion, the experiments and results presented in the paper provide robust evidence supporting the scientific hypotheses related to achieving minimal targeted updates of language models through the innovative approach of Targeted Negative Training (TNT). The findings demonstrate the effectiveness of TNT in reducing unwanted outputs while preserving the model's generation behavior, validating the importance of this method in addressing the challenges associated with undesirable text generation .


What are the contributions of this paper?

The contributions of the paper include addressing the challenges in detoxifying language models , exploring controlled text generation with future discriminators , and investigating fine-tuning language models from human preferences . Additionally, the paper delves into the empirical study of catastrophic forgetting in large language models during continual fine-tuning and examines controllable text generation with neurally-decomposed oracle .


What work can be continued in depth?

Further research in the field of language models can be expanded by delving deeper into the development of targeted training strategies to enhance model performance. One area of exploration could involve investigating the impact of different training-time techniques on model generations, such as the use of modified data or prompt design . Additionally, exploring the effectiveness of various finetuning methods, like Targeted Negative Training (TNT), in minimizing unwanted outputs while maintaining the original model's behavior could be a valuable avenue for future research . Furthermore, comparing TNT with other related approaches in the literature and conducting experiments to analyze the precision and control it offers over reducing undesired behavior could provide insights for optimizing language model training strategies .


Introduction
Background
Evolution of language models and challenges with unwanted outputs
Importance of reducing hallucinations, toxicity, and disfluencies
Objective
To evaluate and compare TNT methods for better language model behavior
Aim to optimize finetuning and constraint techniques
Methodology
Data Collection
Datasets used:
XSUM: Summarization task
Civil Comments: Toxicity detection
PaLM-2: Large language model evaluation
Data Preprocessing
Cleaning and preprocessing techniques for enhancing model input
Handling class imbalance in unwanted output detection
Targeted Negative Training Techniques
TNT: Fundamentals
Overview of the approach and its advantages
Comparison with existing methods
TNFF (Token-Level Negative Feedback)
Method description and implementation
Performance on reducing unwanted content
TNRR (Token-Level Negative Refinement)
Refinement strategy and its impact on fluency and similarity
Disfluency Detection and Mitigation
Techniques for identifying and addressing disfluencies
Iterative Refinement
Iterative process for improving model performance
Controlled Generation
Strategies to maintain desired output characteristics
Evaluation and Trade-offs
Performance metrics: reduction in unwanted content vs. preservation of fluency and similarity
Low-resource scenarios: challenges and strategies
Impact of model size and complexity
Results and Discussion
Comparative analysis of TNT methods
Case studies on specific datasets
Practical implications and limitations
Conclusion
Summary of findings and contributions
Future directions for research in targeted negative training
The need for more nuanced definitions of unwanted content in real-world applications
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
How does targeted negative training (TNT) compare to existing methods in addressing unwanted outputs?
What are some of the challenges and trade-offs discussed in the research regarding reducing unwanted behavior in language models?
What technique does the research paper collection focus on for improving language models?
What datasets are used for evaluating the effectiveness of TNT methods like TNFF and TNRR?

Towards Minimal Targeted Updates of Language Models with Targeted Negative Training

Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi·June 19, 2024

Summary

This collection of research papers explores various techniques for improving language models, with a focus on targeted negative training (TNT) to reduce unwanted outputs like hallucinations, toxicity, and disfluencies. TNT outperforms existing methods in balancing reduction without significantly altering the model's original behavior. The studies cover topics such as finetuning strategies, optimizing token-level constraints, and addressing bias, with experiments on datasets like XSUM, Civil Comments, and PaLM-2. TNT methods, like TNFF and TNRR, are shown to be effective in reducing unwanted content while maintaining fluency and similarity to the original model. The research also highlights the importance of iterative refinement, controlled generation, and the trade-offs between reducing unwanted behavior and preserving model performance, especially in low-resource scenarios. Some studies address specific issues like disfluency detection and mitigation, and the broader impact of these methods emphasizes the need for more sophisticated definitions of unwanted content in practical applications.
Mind map
Performance on reducing unwanted content
Method description and implementation
Comparison with existing methods
Overview of the approach and its advantages
Strategies to maintain desired output characteristics
Controlled Generation
Iterative process for improving model performance
Iterative Refinement
Techniques for identifying and addressing disfluencies
Disfluency Detection and Mitigation
Refinement strategy and its impact on fluency and similarity
TNRR (Token-Level Negative Refinement)
TNFF (Token-Level Negative Feedback)
TNT: Fundamentals
Impact of model size and complexity
Low-resource scenarios: challenges and strategies
Performance metrics: reduction in unwanted content vs. preservation of fluency and similarity
Targeted Negative Training Techniques
PaLM-2: Large language model evaluation
Civil Comments: Toxicity detection
XSUM: Summarization task
Datasets used:
Aim to optimize finetuning and constraint techniques
To evaluate and compare TNT methods for better language model behavior
Importance of reducing hallucinations, toxicity, and disfluencies
Evolution of language models and challenges with unwanted outputs
The need for more nuanced definitions of unwanted content in real-world applications
Future directions for research in targeted negative training
Summary of findings and contributions
Practical implications and limitations
Case studies on specific datasets
Comparative analysis of TNT methods
Evaluation and Trade-offs
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Discussion
Methodology
Introduction
Outline
Introduction
Background
Evolution of language models and challenges with unwanted outputs
Importance of reducing hallucinations, toxicity, and disfluencies
Objective
To evaluate and compare TNT methods for better language model behavior
Aim to optimize finetuning and constraint techniques
Methodology
Data Collection
Datasets used:
XSUM: Summarization task
Civil Comments: Toxicity detection
PaLM-2: Large language model evaluation
Data Preprocessing
Cleaning and preprocessing techniques for enhancing model input
Handling class imbalance in unwanted output detection
Targeted Negative Training Techniques
TNT: Fundamentals
Overview of the approach and its advantages
Comparison with existing methods
TNFF (Token-Level Negative Feedback)
Method description and implementation
Performance on reducing unwanted content
TNRR (Token-Level Negative Refinement)
Refinement strategy and its impact on fluency and similarity
Disfluency Detection and Mitigation
Techniques for identifying and addressing disfluencies
Iterative Refinement
Iterative process for improving model performance
Controlled Generation
Strategies to maintain desired output characteristics
Evaluation and Trade-offs
Performance metrics: reduction in unwanted content vs. preservation of fluency and similarity
Low-resource scenarios: challenges and strategies
Impact of model size and complexity
Results and Discussion
Comparative analysis of TNT methods
Case studies on specific datasets
Practical implications and limitations
Conclusion
Summary of findings and contributions
Future directions for research in targeted negative training
The need for more nuanced definitions of unwanted content in real-world applications
Key findings
12

Paper digest

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

The paper aims to address the challenge of updating language models to avoid generating undesirable outputs while minimally altering the model's behavior, which is referred to as a minimal targeted update . This problem is not entirely new, as existing strategies like retraining and finetuning have been used to modify models to address specific issues, but they can lead to the emergence of new problems . The proposed approach in the paper, called Targeted Negative Training (TNT), offers a method for making more targeted changes to a model by using negative examples from the model's generations to achieve minimal targeted updates .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to updating language models to avoid generating undesirable outputs while minimally altering the model's behavior, a concept referred to as a minimal targeted update . The research focuses on proposing a method called Targeted Negative Training (TNT) that utilizes negative examples from a model's generations to achieve updates that maintain the new distribution close to the original, unlike existing methods that only push down probability without controlling the updated distribution . The study demonstrates that TNT offers a better balance between reducing unwanted behavior and preserving the model's generation capabilities compared to baseline approaches, paving the way for a modeling paradigm based on iterative training updates that restrict models from producing undesirable outputs while retaining their impressive capabilities .


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

The paper "Towards Minimal Targeted Updates of Language Models with Targeted Negative Training" proposes a method called Targeted Negative Training (TNT) to achieve minimal targeted updates of language models . This method aims to address the challenge of updating a model to avoid undesirable outputs while minimally changing the model's behavior in other aspects . TNT uses negative examples from a model's generations to achieve updates that keep the new distribution close to the original, unlike existing losses for negative signal that only push down probability without controlling the updated distribution .

The proposed TNT method in the paper focuses on training-time strategies to improve a model's generations by minimizing unwanted behavior while preserving the model's impressive capabilities . This approach contrasts with existing techniques that introduce latency or complexity during prediction by pushing all desired model changes to inference time . The paper emphasizes the importance of maintaining the ease of use and speed of language models during prediction, making training-time strategies like TNT crucial for enhancing model generations .

Furthermore, the paper discusses the challenges in detoxifying language models and the need for strategies that can effectively control the generations of existing language models to avoid undesirable outputs . By introducing TNT as a method for minimal targeted updates, the paper contributes to the development of a modeling paradigm based on iterative training updates that constrain models from generating unwanted outputs while preserving their capabilities . The paper "Towards Minimal Targeted Updates of Language Models with Targeted Negative Training" introduces Targeted Negative Training (TNT) as a method to achieve minimal targeted updates of language models . Compared to previous methods, TNT offers several key characteristics and advantages:

  1. Improved Trade-off Between Similarity and Reduction: TNT methods provide a better trade-off between similarity to the original generations and reduction of unwanted behavior compared to baseline methods, especially up to a 50% reduction rate . This indicates that TNT methods excel in balancing the need to reduce unwanted behavior while maintaining similarity to the original model's generations.

  2. Effective Reduction of Unwanted Behavior: TNT methods, such as TNFF, TNRR, and TNRF, demonstrate superior performance in reducing unwanted behavior, particularly in toxicity reduction tasks, where they outperform baseline methods across all levels of toxicity rate reduction . This highlights the effectiveness of TNT in achieving targeted updates to minimize undesirable outputs.

  3. Minimization of Introduced Disfluencies: Even when achieving comparable similarity and reduction rates as baselines, TNT methods are significantly better at avoiding the introduction of new disfluencies in model generations . This indicates that TNT methods prioritize maintaining the quality of model outputs by minimizing the introduction of obvious disfluencies.

  4. Training-time Strategies for Model Improvement: Unlike techniques that introduce latency or complexity during prediction by pushing desired changes to inference time, TNT focuses on training-time strategies to enhance model generations . This approach ensures that model updates are integrated seamlessly without compromising the ease of use and speed of language models during prediction.

  5. Iterative Refinement of Models: TNT offers a means to iteratively refine a model after its initial training, contributing to the safety and reliability of autoregressive generative models . By targeting updates to avoid unwanted behavior in a targeted fashion, TNT enables continuous improvement of model behavior without the need for complete model retraining.

  6. Consideration of Model Size and Data Volume: TNT methods show promise in maintaining model behavior even with less data, suggesting practical efficacy for minimal targeted updates in low-data regimes . Additionally, TNT methods demonstrate a better trade-off between similarity and reduction as dataset size decreases, highlighting their adaptability to varying data volumes.

In conclusion, the characteristics and advantages of Targeted Negative Training (TNT) outlined in the paper emphasize its effectiveness in achieving minimal targeted updates of language models by balancing the reduction of unwanted behavior with the preservation of model capabilities and generation quality.


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 language models and text generation. Noteworthy researchers in this area include Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, Po-Sen Huang, Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, Yejin Choi, and many others .

The key to the solution mentioned in the paper "Towards Minimal Targeted Updates of Language Models with Targeted Negative Training" involves a finetuning procedure that only requires training one model using analytically computable token-level divergences. This approach focuses on pointwise constraints and aims to optimize analytical token-level divergences even with sequence-level annotations, providing a simpler algorithm compared to existing methods .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of different methods for minimal targeted updates of language models. The experiments involved comparing various techniques to reduce hallucination and toxicity rates in text generation while maintaining similarity to the original generations . These methods included NL+LL, UL+LL, TNRLL, TNFLL, and TNRLL, each with different parameters and objectives aimed at minimizing unwanted behaviors in generated text . The experiments also assessed the impact of dataset size on the effectiveness of the targeted update methods, showing that some methods performed better with less data, highlighting their practical efficacy in low-data regimes . The paper's experimental design focused on analyzing the trade-off between reducing unwanted behaviors like hallucination and toxicity while preserving the original model's behavior and minimizing obvious disfluencies in the generated text .


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

The dataset used for quantitative evaluation in the study is the Civil Comments dataset of online comments . The code used for determining whether a hallucination exists in the generated summary is open source and can be found in the code from Nan et al. (2021) .


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 "Towards Minimal Targeted Updates of Language Models with Targeted Negative Training" provide strong support for the scientific hypotheses that needed verification. The study introduces the concept of a minimal targeted update and proposes Targeted Negative Training (TNT) as a method to achieve such updates by using negative examples from a model's generations . The results demonstrate that TNT yields a better trade-off between reducing unwanted behavior, such as hallucinations and toxicity, while maintaining the model's generation behavior compared to baseline methods . This indicates that the proposed TNT method effectively minimizes unwanted outputs while preserving the impressive capabilities of language models, aligning with the scientific hypotheses of achieving minimal targeted updates .

Furthermore, the experiments conducted in the study show that TNT methods outperform baseline methods up to a 50% reduction rate in terms of similarity vs. reduction, highlighting the effectiveness of TNT in achieving the desired balance . The results also indicate that TNT methods struggle to reduce the hallucination rate beyond a certain point, but baseline methods achieve this at the expense of increasing obvious disfluencies . This analysis supports the hypothesis that TNT strikes a better balance between reducing unwanted behavior and maintaining model generation behavior, emphasizing the significance of the proposed method in achieving minimal targeted updates .

In conclusion, the experiments and results presented in the paper provide robust evidence supporting the scientific hypotheses related to achieving minimal targeted updates of language models through the innovative approach of Targeted Negative Training (TNT). The findings demonstrate the effectiveness of TNT in reducing unwanted outputs while preserving the model's generation behavior, validating the importance of this method in addressing the challenges associated with undesirable text generation .


What are the contributions of this paper?

The contributions of the paper include addressing the challenges in detoxifying language models , exploring controlled text generation with future discriminators , and investigating fine-tuning language models from human preferences . Additionally, the paper delves into the empirical study of catastrophic forgetting in large language models during continual fine-tuning and examines controllable text generation with neurally-decomposed oracle .


What work can be continued in depth?

Further research in the field of language models can be expanded by delving deeper into the development of targeted training strategies to enhance model performance. One area of exploration could involve investigating the impact of different training-time techniques on model generations, such as the use of modified data or prompt design . Additionally, exploring the effectiveness of various finetuning methods, like Targeted Negative Training (TNT), in minimizing unwanted outputs while maintaining the original model's behavior could be a valuable avenue for future research . Furthermore, comparing TNT with other related approaches in the literature and conducting experiments to analyze the precision and control it offers over reducing undesired behavior could provide insights for optimizing language model training strategies .

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