HARE: HumAn pRiors, a key to small language model Efficiency

Lingyun Zhang, Bin jin, Gaojian Ge, Lunhui Liu, Xuewen Shen, Mingyong Wu, Houqian Zhang, Yongneng Jiang, Shiqi Chen, Shi Pu·June 17, 2024

Summary

The paper investigates the role of human priors in training small language models (SLMs) for efficient performance in resource-constrained environments. It proposes a data construction principle that combines high-quality, semantically diverse, and consistent data by synthesizing with large language models and incorporating human knowledge. The HARE-1.1B model, developed following this principle, outperforms state-of-the-art SLMs on benchmark datasets, showing the effectiveness of leveraging human priors. The research also highlights a data decontamination process to minimize benchmark data leakage and presents a method for combining open-source, synthetic, and task-specific data for improved model capabilities. HARE-1.1B's success demonstrates the potential of human-informed approaches in enhancing model performance and efficiency, while also addressing limitations in data quality and biases.

Key findings

5

Paper digest

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

The paper aims to address the issue of inefficient training of Small Language Models (SLMs) in resource-constrained settings due to the neglect of human priors in data construction . This problem is not entirely new, as existing SLMs have been heavily relying on web-scraped large-scale data, which limits their training efficiency in such environments . The paper proposes a method to incorporate human priors into data construction for training SLMs, emphasizing semantic diversity, data quality consistency, and avoiding benchmark data leakage .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that incorporating human priors into data construction for training Small Language Models (SLMs) enhances model capabilities without introducing benchmark data leakage issues . The study aims to explore how different data combinations affect model performance, focusing on enhancing semantic diversity, data quality consistency, and NLP-task solving capabilities . The proposed data construction method ensures both semantic diversity and data quality consistency while avoiding benchmark data leakage, ultimately improving the performance and generalization of the SLMs .


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

The paper "HARE: HumAn pRiors, a key to small language model Efficiency" proposes several innovative ideas, methods, and models in the field of small language models (SLMs) . Here are some key points from the paper:

  1. Principle of Leveraging Human Priors for Data Construction: The paper introduces a principle that emphasizes the importance of incorporating human priors in data construction for training efficient SLMs . This principle focuses on achieving high-performance SLMs by training on a concise dataset that balances semantic diversity and data quality consistency while avoiding benchmark data leakage.

  2. Development of HARE-1.1B Model: The paper presents the development of the HARE-1.1B model, a small language model trained on a dataset that integrates human priors in data construction . The model consists of 22 hidden layers, 32 attention heads, and 8 key-value heads, with specific parameters optimized for efficiency.

  3. Data Synthesis for NLP Tasks: The paper describes the process of synthesizing a substantial amount of NLP-task data in natural language form to enhance NLP-task solving capabilities . This involves creating diverse prompts and using seed data to guide the synthesis of NLP-task data, along with collecting open-source NLP-task datasets to expand the dataset.

  4. Data Decontamination Process: To ensure the generated data does not pose a risk of benchmark data leakage, the paper outlines a rigorous data decontamination process . This process involves statistical analyses to remove samples that do not meet standards, n-gram overlap calculations with benchmark data, and the establishment of a final training dataset consisting of high-quality categorized data, synthetic data, and a mixture of synthetic and open-source NLP task data.

  5. Training Process and Experiments: The paper details the training process of the HARE-1.1B model, which spans 30 days and processes 600 billion tokens . The training involves two stages based on different data sources, with the model trained on a combination of datasets to enhance performance. The paper also conducts ablation studies and comparisons with state-of-the-art SLMs on benchmark datasets to validate the effectiveness of the proposed data construction method .

Overall, the paper introduces a comprehensive approach to training efficient small language models by leveraging human priors in data construction, synthesizing data for NLP tasks, implementing a data decontamination process, and conducting rigorous training and experiments to validate the proposed methods and models . The paper "HARE: HumAn pRiors, a key to small language model Efficiency" introduces a novel approach to training small language models (SLMs) by incorporating human priors in data construction, ensuring semantic diversity, data quality consistency, and avoiding benchmark data leakage . This method offers several characteristics and advantages compared to previous methods:

  1. Data Construction Pipeline: The paper outlines a comprehensive data construction pipeline that involves cleaning open-source pre-training corpora using heuristic rules, categorizing datasets, adjusting sampling weights, and ensuring high-quality data consistency . This meticulous data cleaning process enhances the quality of the training data, setting a strong foundation for model training.

  2. Data Synthesis Using LLMs: The approach includes data synthesis using large language models (LLMs) to address semantic ambiguities in the cleaned data . By clustering data into various topics, sampling seed data, and inputting diverse prompts into LLMs for synthesis, the method significantly enhances semantic diversity while maintaining consistent data quality.

  3. Incorporation of Human Priors: The key advantage lies in the incorporation of human priors into the training data, leading to models trained under better conditions, thereby enhancing performance and generalization . This approach helps achieve outstanding performance on benchmark datasets by guiding the model with essential human priors.

  4. Avoidance of Benchmark Data Leakage: The proposed method effectively avoids benchmark data leakage issues that may arise from injecting excessive human priors, as observed in some recent SLMs . By ensuring semantic diversity, data quality consistency, and strict decontamination procedures, the model maintains relatively low ∆ values across benchmark evaluations, reducing the risk of data leakage.

  5. Performance Validation: The HARE-1.1B model, trained using the proposed method, performs favorably against existing state-of-the-art SLMs on large-scale benchmark datasets, validating the effectiveness of the data construction approach . The model demonstrates strong generalization capabilities and maintains low ∆ values, indicating a lower probability of data leakage.

In conclusion, the innovative characteristics of incorporating human priors, meticulous data construction, data synthesis using LLMs, and the emphasis on semantic diversity and data quality consistency set the proposed method apart from previous approaches, offering enhanced model performance and reduced risk of benchmark data leakage .


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 papers and notable researchers in the field of small language model efficiency have been mentioned in the context:

  • "Free dolly: Introducing the world’s first truly open instruction-tuned llm" by Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin .
  • "Enhancing chat language models by scaling high-quality instructional conversations" by Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou .
  • "Mistral of experts" by Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. .
  • "Textbooks are all you need ii: phi-1.5 technical report" by Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee .

The key solution mentioned in the paper "HARE: HumAn pRiors, a key to small language model Efficiency" is the proposal to leverage human priors for data construction in small language models. This principle emphasizes training on a concise dataset that balances semantic diversity and data quality consistency while avoiding benchmark data leakage. By incorporating human priors effectively, the proposed principle aims to achieve high-performance small language models (SLMs) in resource-constrained settings .


How were the experiments in the paper designed?

The experiments in the paper were designed to include ablation studies and comparisons with state-of-the-art small language models (SLMs) . These experiments involved conducting ablation studies on a 0.25B model to validate the effectiveness of the proposed data construction method . Three experimental groups were established: training on the D1 dataset, training on the combination of the D1 and D2 datasets, and training on the integration of the D1, D2, and D3 datasets . The experiments aimed to efficiently validate the model's capabilities and the impact of the data construction method on model performance .


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

The dataset used for quantitative evaluation in the study is the Mistral architecture . The code for Mistral is open source, as it is mentioned in the context that Mistral is utilized in the study .


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

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study proposed encoding human priors into data construction for training Small Language Models (SLMs) to enhance model capabilities without introducing benchmark data leakage issues . The data construction method ensured semantic diversity, data quality consistency, and avoided benchmark data leakage . The experiments conducted ablation studies and comparisons with State-of-the-Art (SOTA) SLMs, demonstrating the effectiveness of the proposed data construction method . The results showed that the model maintained relatively low ∆ values across benchmark evaluations, indicating a lower probability of data leakage compared to other SLMs . Additionally, the study compared the performance of the HARE-1.1B model with 9 other SLMs on the Open LLM Leaderboard, showing that the model outperformed models trained with web-scraped large-scale data and synthetic data lacking NLP-task data . These findings validate the effectiveness of incorporating human priors into the data construction process for training SLMs and achieving favorable model performance .


What are the contributions of this paper?

The paper "HARE: HumAn pRiors, a key to small language model Efficiency" proposes a principle to leverage human priors for data construction in small language models (SLMs) . The key contributions of this paper include:

  • Proposing a principle that emphasizes training SLMs on a concise dataset accommodating semantic diversity and data quality consistency while avoiding benchmark data leakage .
  • Introducing the SLM named HARE-1.1B, which outperforms state-of-the-art SLMs on large-scale benchmark datasets, showcasing the effectiveness of incorporating human priors in data construction .
  • Providing insights into efficient language model training in resource-constrained environments by highlighting the importance of human priors in data construction .

What work can be continued in depth?

To delve deeper into the research, further exploration can be conducted on the integration of human priors in data construction for small language models (SLMs) to enhance performance and generalization . This involves investigating the impact of injecting human priors on data quality, semantic diversity, and the prevention of benchmark data leakage . Additionally, studying the effectiveness of different methods, such as clustering web-scraped data into topics and using diverse prompts for data synthesis, can provide insights into improving model generalization . Further research can focus on the development of data construction principles that balance semantic diversity, data quality consistency, and the avoidance of benchmark data leakage in SLM training .

Tables

4

Introduction
Background
Emergence of small language models in resource-constrained scenarios
Challenges with data quality and biases in SLMs
Objective
To explore the impact of human priors on SLM performance
Develop a data construction principle for efficient and unbiased models
Method
Data Construction Principle
Synthesizing with Large Language Models
Utilizing LLMs for semantic guidance and consistency
Incorporating Human Knowledge
Expert annotations and domain-specific insights
HARE-1.1B Model Development
Detailed methodology and implementation
Data Collection
Selection of high-quality source data
Large language model assistance for data augmentation
Human-in-the-loop data curation
Data Preprocessing
Decontamination process to address benchmark data leakage
Data cleaning and standardization
Synthetic data integration
Experiments and Results
HARE-1.1B Performance Evaluation
Benchmark dataset comparisons
State-of-the-art SLMs surpassed
Efficiency and accuracy improvements
Data Combination Strategy
Open-source, synthetic, and task-specific data fusion
Impact on model generalization and adaptability
Discussion
Human priors' impact on model performance and efficiency
Addressing data quality and bias limitations
Future directions for human-informed model development
Conclusion
HARE-1.1B's success as a proof of concept
The potential of human-informed approaches in SLMs
Recommendations for practical applications in resource-constrained environments
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What principle does the paper propose for constructing data for training small language models?
How does the use of human priors contribute to the efficiency of resource-constrained language models?
What is the purpose of the data decontamination process mentioned in the research?
How does the HARE-1.1B model compare to state-of-the-art SLMs in terms of performance?

HARE: HumAn pRiors, a key to small language model Efficiency

Lingyun Zhang, Bin jin, Gaojian Ge, Lunhui Liu, Xuewen Shen, Mingyong Wu, Houqian Zhang, Yongneng Jiang, Shiqi Chen, Shi Pu·June 17, 2024

Summary

The paper investigates the role of human priors in training small language models (SLMs) for efficient performance in resource-constrained environments. It proposes a data construction principle that combines high-quality, semantically diverse, and consistent data by synthesizing with large language models and incorporating human knowledge. The HARE-1.1B model, developed following this principle, outperforms state-of-the-art SLMs on benchmark datasets, showing the effectiveness of leveraging human priors. The research also highlights a data decontamination process to minimize benchmark data leakage and presents a method for combining open-source, synthetic, and task-specific data for improved model capabilities. HARE-1.1B's success demonstrates the potential of human-informed approaches in enhancing model performance and efficiency, while also addressing limitations in data quality and biases.
Mind map
Detailed methodology and implementation
Expert annotations and domain-specific insights
Utilizing LLMs for semantic guidance and consistency
Impact on model generalization and adaptability
Open-source, synthetic, and task-specific data fusion
Efficiency and accuracy improvements
State-of-the-art SLMs surpassed
Benchmark dataset comparisons
Synthetic data integration
Data cleaning and standardization
Decontamination process to address benchmark data leakage
Human-in-the-loop data curation
Large language model assistance for data augmentation
Selection of high-quality source data
HARE-1.1B Model Development
Incorporating Human Knowledge
Synthesizing with Large Language Models
Develop a data construction principle for efficient and unbiased models
To explore the impact of human priors on SLM performance
Challenges with data quality and biases in SLMs
Emergence of small language models in resource-constrained scenarios
Recommendations for practical applications in resource-constrained environments
The potential of human-informed approaches in SLMs
HARE-1.1B's success as a proof of concept
Future directions for human-informed model development
Addressing data quality and bias limitations
Human priors' impact on model performance and efficiency
Data Combination Strategy
HARE-1.1B Performance Evaluation
Data Preprocessing
Data Collection
Data Construction Principle
Objective
Background
Conclusion
Discussion
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Emergence of small language models in resource-constrained scenarios
Challenges with data quality and biases in SLMs
Objective
To explore the impact of human priors on SLM performance
Develop a data construction principle for efficient and unbiased models
Method
Data Construction Principle
Synthesizing with Large Language Models
Utilizing LLMs for semantic guidance and consistency
Incorporating Human Knowledge
Expert annotations and domain-specific insights
HARE-1.1B Model Development
Detailed methodology and implementation
Data Collection
Selection of high-quality source data
Large language model assistance for data augmentation
Human-in-the-loop data curation
Data Preprocessing
Decontamination process to address benchmark data leakage
Data cleaning and standardization
Synthetic data integration
Experiments and Results
HARE-1.1B Performance Evaluation
Benchmark dataset comparisons
State-of-the-art SLMs surpassed
Efficiency and accuracy improvements
Data Combination Strategy
Open-source, synthetic, and task-specific data fusion
Impact on model generalization and adaptability
Discussion
Human priors' impact on model performance and efficiency
Addressing data quality and bias limitations
Future directions for human-informed model development
Conclusion
HARE-1.1B's success as a proof of concept
The potential of human-informed approaches in SLMs
Recommendations for practical applications in resource-constrained environments
Key findings
5

Paper digest

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

The paper aims to address the issue of inefficient training of Small Language Models (SLMs) in resource-constrained settings due to the neglect of human priors in data construction . This problem is not entirely new, as existing SLMs have been heavily relying on web-scraped large-scale data, which limits their training efficiency in such environments . The paper proposes a method to incorporate human priors into data construction for training SLMs, emphasizing semantic diversity, data quality consistency, and avoiding benchmark data leakage .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that incorporating human priors into data construction for training Small Language Models (SLMs) enhances model capabilities without introducing benchmark data leakage issues . The study aims to explore how different data combinations affect model performance, focusing on enhancing semantic diversity, data quality consistency, and NLP-task solving capabilities . The proposed data construction method ensures both semantic diversity and data quality consistency while avoiding benchmark data leakage, ultimately improving the performance and generalization of the SLMs .


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

The paper "HARE: HumAn pRiors, a key to small language model Efficiency" proposes several innovative ideas, methods, and models in the field of small language models (SLMs) . Here are some key points from the paper:

  1. Principle of Leveraging Human Priors for Data Construction: The paper introduces a principle that emphasizes the importance of incorporating human priors in data construction for training efficient SLMs . This principle focuses on achieving high-performance SLMs by training on a concise dataset that balances semantic diversity and data quality consistency while avoiding benchmark data leakage.

  2. Development of HARE-1.1B Model: The paper presents the development of the HARE-1.1B model, a small language model trained on a dataset that integrates human priors in data construction . The model consists of 22 hidden layers, 32 attention heads, and 8 key-value heads, with specific parameters optimized for efficiency.

  3. Data Synthesis for NLP Tasks: The paper describes the process of synthesizing a substantial amount of NLP-task data in natural language form to enhance NLP-task solving capabilities . This involves creating diverse prompts and using seed data to guide the synthesis of NLP-task data, along with collecting open-source NLP-task datasets to expand the dataset.

  4. Data Decontamination Process: To ensure the generated data does not pose a risk of benchmark data leakage, the paper outlines a rigorous data decontamination process . This process involves statistical analyses to remove samples that do not meet standards, n-gram overlap calculations with benchmark data, and the establishment of a final training dataset consisting of high-quality categorized data, synthetic data, and a mixture of synthetic and open-source NLP task data.

  5. Training Process and Experiments: The paper details the training process of the HARE-1.1B model, which spans 30 days and processes 600 billion tokens . The training involves two stages based on different data sources, with the model trained on a combination of datasets to enhance performance. The paper also conducts ablation studies and comparisons with state-of-the-art SLMs on benchmark datasets to validate the effectiveness of the proposed data construction method .

Overall, the paper introduces a comprehensive approach to training efficient small language models by leveraging human priors in data construction, synthesizing data for NLP tasks, implementing a data decontamination process, and conducting rigorous training and experiments to validate the proposed methods and models . The paper "HARE: HumAn pRiors, a key to small language model Efficiency" introduces a novel approach to training small language models (SLMs) by incorporating human priors in data construction, ensuring semantic diversity, data quality consistency, and avoiding benchmark data leakage . This method offers several characteristics and advantages compared to previous methods:

  1. Data Construction Pipeline: The paper outlines a comprehensive data construction pipeline that involves cleaning open-source pre-training corpora using heuristic rules, categorizing datasets, adjusting sampling weights, and ensuring high-quality data consistency . This meticulous data cleaning process enhances the quality of the training data, setting a strong foundation for model training.

  2. Data Synthesis Using LLMs: The approach includes data synthesis using large language models (LLMs) to address semantic ambiguities in the cleaned data . By clustering data into various topics, sampling seed data, and inputting diverse prompts into LLMs for synthesis, the method significantly enhances semantic diversity while maintaining consistent data quality.

  3. Incorporation of Human Priors: The key advantage lies in the incorporation of human priors into the training data, leading to models trained under better conditions, thereby enhancing performance and generalization . This approach helps achieve outstanding performance on benchmark datasets by guiding the model with essential human priors.

  4. Avoidance of Benchmark Data Leakage: The proposed method effectively avoids benchmark data leakage issues that may arise from injecting excessive human priors, as observed in some recent SLMs . By ensuring semantic diversity, data quality consistency, and strict decontamination procedures, the model maintains relatively low ∆ values across benchmark evaluations, reducing the risk of data leakage.

  5. Performance Validation: The HARE-1.1B model, trained using the proposed method, performs favorably against existing state-of-the-art SLMs on large-scale benchmark datasets, validating the effectiveness of the data construction approach . The model demonstrates strong generalization capabilities and maintains low ∆ values, indicating a lower probability of data leakage.

In conclusion, the innovative characteristics of incorporating human priors, meticulous data construction, data synthesis using LLMs, and the emphasis on semantic diversity and data quality consistency set the proposed method apart from previous approaches, offering enhanced model performance and reduced risk of benchmark data leakage .


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 papers and notable researchers in the field of small language model efficiency have been mentioned in the context:

  • "Free dolly: Introducing the world’s first truly open instruction-tuned llm" by Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin .
  • "Enhancing chat language models by scaling high-quality instructional conversations" by Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou .
  • "Mistral of experts" by Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. .
  • "Textbooks are all you need ii: phi-1.5 technical report" by Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee .

The key solution mentioned in the paper "HARE: HumAn pRiors, a key to small language model Efficiency" is the proposal to leverage human priors for data construction in small language models. This principle emphasizes training on a concise dataset that balances semantic diversity and data quality consistency while avoiding benchmark data leakage. By incorporating human priors effectively, the proposed principle aims to achieve high-performance small language models (SLMs) in resource-constrained settings .


How were the experiments in the paper designed?

The experiments in the paper were designed to include ablation studies and comparisons with state-of-the-art small language models (SLMs) . These experiments involved conducting ablation studies on a 0.25B model to validate the effectiveness of the proposed data construction method . Three experimental groups were established: training on the D1 dataset, training on the combination of the D1 and D2 datasets, and training on the integration of the D1, D2, and D3 datasets . The experiments aimed to efficiently validate the model's capabilities and the impact of the data construction method on model performance .


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

The dataset used for quantitative evaluation in the study is the Mistral architecture . The code for Mistral is open source, as it is mentioned in the context that Mistral is utilized in the study .


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

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study proposed encoding human priors into data construction for training Small Language Models (SLMs) to enhance model capabilities without introducing benchmark data leakage issues . The data construction method ensured semantic diversity, data quality consistency, and avoided benchmark data leakage . The experiments conducted ablation studies and comparisons with State-of-the-Art (SOTA) SLMs, demonstrating the effectiveness of the proposed data construction method . The results showed that the model maintained relatively low ∆ values across benchmark evaluations, indicating a lower probability of data leakage compared to other SLMs . Additionally, the study compared the performance of the HARE-1.1B model with 9 other SLMs on the Open LLM Leaderboard, showing that the model outperformed models trained with web-scraped large-scale data and synthetic data lacking NLP-task data . These findings validate the effectiveness of incorporating human priors into the data construction process for training SLMs and achieving favorable model performance .


What are the contributions of this paper?

The paper "HARE: HumAn pRiors, a key to small language model Efficiency" proposes a principle to leverage human priors for data construction in small language models (SLMs) . The key contributions of this paper include:

  • Proposing a principle that emphasizes training SLMs on a concise dataset accommodating semantic diversity and data quality consistency while avoiding benchmark data leakage .
  • Introducing the SLM named HARE-1.1B, which outperforms state-of-the-art SLMs on large-scale benchmark datasets, showcasing the effectiveness of incorporating human priors in data construction .
  • Providing insights into efficient language model training in resource-constrained environments by highlighting the importance of human priors in data construction .

What work can be continued in depth?

To delve deeper into the research, further exploration can be conducted on the integration of human priors in data construction for small language models (SLMs) to enhance performance and generalization . This involves investigating the impact of injecting human priors on data quality, semantic diversity, and the prevention of benchmark data leakage . Additionally, studying the effectiveness of different methods, such as clustering web-scraped data into topics and using diverse prompts for data synthesis, can provide insights into improving model generalization . Further research can focus on the development of data construction principles that balance semantic diversity, data quality consistency, and the avoidance of benchmark data leakage in SLM training .

Tables
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