BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation

Minchong Li, Feng Zhou, Xiaohui Song·June 19, 2024

Summary

This paper investigates the task-specific distillation of large language models, revealing that their logits have a distinct long-tail distribution compared to vision models. Existing methods struggle with utilizing ranking information effectively. The authors introduce the Bi-directional Logits Difference (BiLD) loss, which filters out noise using top-k logits and focuses on internal ranking. BiLD outperforms various techniques, including fine-tuning and CV/NLP distillation, on 13 datasets with BLOOM and Qwen1.5 LLMs, even with limited top-8 logits. The study highlights BiLD's ability to emulate teacher behavior and suggests it as a superior choice for LLM distillation by addressing the complexities of LLM output spaces and leveraging ranking information.

Key findings

9

Paper digest

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

The paper addresses the challenge of distilling knowledge from large language models (LLMs) to smaller models, specifically focusing on distillation at the logit level to enhance performance and reduce model size . This problem is not entirely new, as knowledge distillation (KD) has been a classic method for compressing models by transferring knowledge from a large teacher model to a smaller student model . The paper introduces the Bi-directional Logits Difference (BiLD) loss as a novel approach to address the limitations of existing logits distillation methods, particularly in the context of LLMs .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the effectiveness of the proposed Bi-directional Logits Difference (BiLD) loss in distilling knowledge from large language models (LLMs) at the logit level . The study investigates how the BiLD loss can filter out long-tail noise in the logits, utilize top-k teacher and student logits, and leverage internal logits ranking information to enhance the alignment of student logits with important parts of teacher logits . The research demonstrates that the BiLD loss, particularly with only the top-8 logits, outperforms other distillation methods in both natural language processing (NLP) and computer vision (CV) fields .


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

The paper "BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation" introduces a novel approach called Bi-directional Logits Difference (BiLD) loss for distilling large language models (LLMs) . This method focuses on distilling LLMs at the logit level by addressing challenges related to the long-tail distribution of logits and the utilization of internal ranking information . The BiLD loss filters out long-tail noise by using only the top-k teacher and student logits and constructs logits differences to leverage internal ranking information . The paper conducts comprehensive experiments on 13 datasets using two types of LLMs, demonstrating that the BiLD loss outperforms other distillation methods from both NLP and CV fields . Additionally, the paper explores the impact of temperature and the k value in the BiLD loss, highlighting the significance of these parameters in achieving optimal distillation results . The proposed BiLD loss enhances the alignment of student logits with important parts of teacher logits, leading to improved distillation performance . Overall, the paper presents innovative ideas and methods for enhancing the distillation of LLMs by addressing specific challenges related to logit distribution and ranking information . The Bi-directional Logits Difference (BiLD) loss proposed in the paper "BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation" offers several key characteristics and advantages compared to previous distillation methods .

  1. Characteristics:

    • The BiLD loss focuses on distilling large language models (LLMs) at the logit level, specifically addressing the challenges posed by the long-tail distribution of logits and the utilization of internal ranking information .
    • It filters out long-tail noise by using only the top-k teacher and student logits and constructs logits differences to leverage internal ranking information .
    • The method customizes temperatures during the distillation process, optimizing the performance of the BiLD loss .
    • BiLD loss significantly enhances the alignment of student logits with important parts of teacher logits, leading to improved distillation performance .
  2. Advantages Compared to Previous Methods:

    • The BiLD loss outperforms other distillation methods from both Natural Language Processing (NLP) and Computer Vision (CV) fields, including supervised fine-tuning (SFT), vanilla KL loss, and five other methods, demonstrating its superior performance .
    • It achieves better distillation performance with an acceptable increase in training time compared to methods like DKD and NKD, which have slower computation speeds due to the calculation of numerous intermediate variables .
    • The BiLD loss notably enhances the overlap@8 metric while maintaining competitive overlap@1, indicating that it helps student logits align with the important parts of teacher logits more effectively than other methods .
    • By focusing on the key knowledge in teacher logits without introducing excessive hyperparameters, the BiLD loss offers a practical and straightforward approach to distillation for LLMs .

In summary, the Bi-directional Logits Difference (BiLD) loss stands out for its innovative approach to distilling large language models, addressing specific challenges related to logit distribution and ranking information, and demonstrating superior performance compared to existing distillation methods in both NLP and CV fields .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of large language model distillation. Noteworthy researchers in this area include Tushar Khot, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Xiao Cui, Yulei Qin, Marie-Catherine De Marneffe, Mandy Simons, Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh, Yao Fu, Hao Peng, Litu Ou, Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, William B Dolan, Yuxian Gu, Li Dong, Furu Wei, Minlie Huang, Gaurav Sahu, Olga Vechtomova, Dzmitry Bahdanau, Issam H Laradji, Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Shangquan Sun, Wenqi Ren, Jingzhi Li, Rui Wang, Xiaochun Cao, among others .

The key to the solution mentioned in the paper "BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation" is the proposal of the Bi-directional Logits Difference (BiLD) loss. This method filters out long-tail noise by utilizing only the top-k teacher and student logits, and leverages internal logits ranking information by constructing logits differences. The BiLD loss aims to address the challenges faced by existing logits distillation methods in effectively utilizing the internal ranking information from the logits, particularly in the context of large language models .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the proposed Bi-directional Logits Difference (BiLD) loss in distilling large language models (LLMs) . The experiments involved conducting comprehensive evaluations on 13 datasets using two types of LLMs . The results of the experiments showed that the BiLD loss outperformed supervised fine-tuning (SFT), vanilla KL loss, and five other distillation methods from both natural language processing (NLP) and computer vision (CV) fields . Additionally, the experiments demonstrated that the BiLD loss notably enhanced the overlap@8 metric while maintaining competitive overlap@1, indicating that the proposed loss helped align student logits with important parts of teacher logits .


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

The dataset used for quantitative evaluation in the study is the BLOOM and Qwen1.5 models, with BLOOM-7B and Qwen-4B being selected as teacher models, and BLOOM-3B and BLOOM-1B as student models . The code for the study will be made available soon .


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 the task-specific distillation of Large Language Models (LLMs) at the logit level and introduces the Bi-directional Logits Difference (BiLD) loss as a method to enhance distillation performance . The results demonstrate that the BiLD loss notably enhances overlap@8 while maintaining competitive overlap@1, indicating that it helps align student logits with important teacher logits . Additionally, the BiLD loss outperforms other distillation methods from both Natural Language Processing (NLP) and Computer Vision (CV) fields, showing superior effectiveness .

Moreover, the experimental results across all 13 datasets consistently show that the BiLD loss achieves the highest average accuracy compared to other methods tested, including supervised fine-tuning and vanilla KL loss . The study reports significant improvements in average accuracy with the BiLD loss in various distillation scenarios, highlighting its robust performance and effectiveness in knowledge distillation . The analysis of the effectiveness of clipping logits indicates that filtering out noise in the long-tail distribution of logits can enhance distillation performance, further supporting the efficacy of the BiLD loss .

Overall, the experiments conducted in the paper, along with the results and analysis presented, provide compelling evidence to support the scientific hypotheses related to the distillation of Large Language Models using the Bi-directional Logits Difference (BiLD) loss. The study's findings demonstrate the effectiveness and superiority of the BiLD loss in enhancing distillation performance and aligning student logits with important teacher logits, contributing significantly to the field of knowledge distillation for LLMs .


What are the contributions of this paper?

The paper "BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation" makes several key contributions in the field of knowledge distillation for large language models (LLMs) . The main contributions of this paper include:

  • Introduction of Bi-directional Logits Difference (BiLD) loss: The paper proposes the BiLD loss, which filters out long-tail noise in the logits of LLMs and leverages internal ranking information to improve distillation performance .
  • Enhanced distillation performance: Through comprehensive experiments on 13 datasets using two types of LLMs, the BiLD loss with only the top-8 logits outperformed other distillation methods from both natural language processing (NLP) and computer vision (CV) fields .
  • Improved alignment of student and teacher logits: The BiLD loss helps student logits align with the important parts of teacher logits, leading to better imitation of the teacher's primary behaviors at the logit level .
  • Addressing challenges: The paper acknowledges challenges such as computational complexity and loss of knowledge in the long-tail distribution of logits, highlighting avenues for future research to better utilize hidden knowledge in the long-tail distribution .

What work can be continued in depth?

Further research in the field of large language model distillation can be expanded by delving into the optimization objective proposed in the Bi-directional Logits Difference (BiLD) loss . This novel approach aims to enhance distillation performance by filtering out long-tail noise and leveraging internal logits ranking information . The BiLD loss has shown superior distillation performance using only the top-8 logits compared to other distillation methods, demonstrating its effectiveness in capturing key knowledge from the teacher model . Expanding on the study of the impact of temperature and the k value in the BiLD loss could provide valuable insights into further optimizing the distillation process .

Tables

3

Introduction
Background
Comparison of LLMs with vision models: Logits distribution uniqueness
Challenges faced by existing distillation methods in LLMs
Objective
To develop an effective distillation technique for LLMs
Introduce Bi-directional Logits Difference (BiLD) loss
Improve performance on various datasets with limited information
Method
Data Collection
Selection of BLOOM and Qwen1.5 LLMs for experimentation
Datasets: 13 diverse language and cross-modal datasets
Data Preprocessing
Analysis of long-tail distribution in LLM logits
Noise filtering using top-k approach
Bi-directional Logits Difference Loss (BiLD)
Top-k Logits Filtering
Identifying and focusing on informative parts of the output
Internal Ranking Utilization
Leveraging the ranking information within the logits
Noise Reduction
Removing irrelevant or noisy ranking signals
Loss Function Formulation
Combining bidirectional differences for effective learning
Experiments and Evaluation
Performance comparison with fine-tuning and CV/NLP distillation techniques
Evaluation on different tasks and model sizes
Impact of limited top-k logits
Results and Discussion
BiLD's superior performance on 13 datasets
Emanating teacher behavior in LLM distillation
Addressing complexities of LLM output spaces
Conclusion
BiLD as a robust and efficient method for LLM distillation
Recommendations for future research in large language model optimization
Potential applications in various NLP tasks and cross-modal learning
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What does the paper focus on regarding large language models?
What are the benefits of using BiLD for emulating teacher behavior in LLMs?
How does BiLD compare to existing methods in terms of performance on LLM distillation?
What is the main contribution of the Bi-directional Logits Difference (BiLD) loss introduced in the paper?

BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation

Minchong Li, Feng Zhou, Xiaohui Song·June 19, 2024

Summary

This paper investigates the task-specific distillation of large language models, revealing that their logits have a distinct long-tail distribution compared to vision models. Existing methods struggle with utilizing ranking information effectively. The authors introduce the Bi-directional Logits Difference (BiLD) loss, which filters out noise using top-k logits and focuses on internal ranking. BiLD outperforms various techniques, including fine-tuning and CV/NLP distillation, on 13 datasets with BLOOM and Qwen1.5 LLMs, even with limited top-8 logits. The study highlights BiLD's ability to emulate teacher behavior and suggests it as a superior choice for LLM distillation by addressing the complexities of LLM output spaces and leveraging ranking information.
Mind map
Combining bidirectional differences for effective learning
Loss Function Formulation
Removing irrelevant or noisy ranking signals
Noise Reduction
Leveraging the ranking information within the logits
Internal Ranking Utilization
Identifying and focusing on informative parts of the output
Top-k Logits Filtering
Impact of limited top-k logits
Evaluation on different tasks and model sizes
Performance comparison with fine-tuning and CV/NLP distillation techniques
Bi-directional Logits Difference Loss (BiLD)
Datasets: 13 diverse language and cross-modal datasets
Selection of BLOOM and Qwen1.5 LLMs for experimentation
Improve performance on various datasets with limited information
Introduce Bi-directional Logits Difference (BiLD) loss
To develop an effective distillation technique for LLMs
Challenges faced by existing distillation methods in LLMs
Comparison of LLMs with vision models: Logits distribution uniqueness
Potential applications in various NLP tasks and cross-modal learning
Recommendations for future research in large language model optimization
BiLD as a robust and efficient method for LLM distillation
Addressing complexities of LLM output spaces
Emanating teacher behavior in LLM distillation
BiLD's superior performance on 13 datasets
Experiments and Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Comparison of LLMs with vision models: Logits distribution uniqueness
Challenges faced by existing distillation methods in LLMs
Objective
To develop an effective distillation technique for LLMs
Introduce Bi-directional Logits Difference (BiLD) loss
Improve performance on various datasets with limited information
Method
Data Collection
Selection of BLOOM and Qwen1.5 LLMs for experimentation
Datasets: 13 diverse language and cross-modal datasets
Data Preprocessing
Analysis of long-tail distribution in LLM logits
Noise filtering using top-k approach
Bi-directional Logits Difference Loss (BiLD)
Top-k Logits Filtering
Identifying and focusing on informative parts of the output
Internal Ranking Utilization
Leveraging the ranking information within the logits
Noise Reduction
Removing irrelevant or noisy ranking signals
Loss Function Formulation
Combining bidirectional differences for effective learning
Experiments and Evaluation
Performance comparison with fine-tuning and CV/NLP distillation techniques
Evaluation on different tasks and model sizes
Impact of limited top-k logits
Results and Discussion
BiLD's superior performance on 13 datasets
Emanating teacher behavior in LLM distillation
Addressing complexities of LLM output spaces
Conclusion
BiLD as a robust and efficient method for LLM distillation
Recommendations for future research in large language model optimization
Potential applications in various NLP tasks and cross-modal learning
Key findings
9

Paper digest

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

The paper addresses the challenge of distilling knowledge from large language models (LLMs) to smaller models, specifically focusing on distillation at the logit level to enhance performance and reduce model size . This problem is not entirely new, as knowledge distillation (KD) has been a classic method for compressing models by transferring knowledge from a large teacher model to a smaller student model . The paper introduces the Bi-directional Logits Difference (BiLD) loss as a novel approach to address the limitations of existing logits distillation methods, particularly in the context of LLMs .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the effectiveness of the proposed Bi-directional Logits Difference (BiLD) loss in distilling knowledge from large language models (LLMs) at the logit level . The study investigates how the BiLD loss can filter out long-tail noise in the logits, utilize top-k teacher and student logits, and leverage internal logits ranking information to enhance the alignment of student logits with important parts of teacher logits . The research demonstrates that the BiLD loss, particularly with only the top-8 logits, outperforms other distillation methods in both natural language processing (NLP) and computer vision (CV) fields .


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

The paper "BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation" introduces a novel approach called Bi-directional Logits Difference (BiLD) loss for distilling large language models (LLMs) . This method focuses on distilling LLMs at the logit level by addressing challenges related to the long-tail distribution of logits and the utilization of internal ranking information . The BiLD loss filters out long-tail noise by using only the top-k teacher and student logits and constructs logits differences to leverage internal ranking information . The paper conducts comprehensive experiments on 13 datasets using two types of LLMs, demonstrating that the BiLD loss outperforms other distillation methods from both NLP and CV fields . Additionally, the paper explores the impact of temperature and the k value in the BiLD loss, highlighting the significance of these parameters in achieving optimal distillation results . The proposed BiLD loss enhances the alignment of student logits with important parts of teacher logits, leading to improved distillation performance . Overall, the paper presents innovative ideas and methods for enhancing the distillation of LLMs by addressing specific challenges related to logit distribution and ranking information . The Bi-directional Logits Difference (BiLD) loss proposed in the paper "BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation" offers several key characteristics and advantages compared to previous distillation methods .

  1. Characteristics:

    • The BiLD loss focuses on distilling large language models (LLMs) at the logit level, specifically addressing the challenges posed by the long-tail distribution of logits and the utilization of internal ranking information .
    • It filters out long-tail noise by using only the top-k teacher and student logits and constructs logits differences to leverage internal ranking information .
    • The method customizes temperatures during the distillation process, optimizing the performance of the BiLD loss .
    • BiLD loss significantly enhances the alignment of student logits with important parts of teacher logits, leading to improved distillation performance .
  2. Advantages Compared to Previous Methods:

    • The BiLD loss outperforms other distillation methods from both Natural Language Processing (NLP) and Computer Vision (CV) fields, including supervised fine-tuning (SFT), vanilla KL loss, and five other methods, demonstrating its superior performance .
    • It achieves better distillation performance with an acceptable increase in training time compared to methods like DKD and NKD, which have slower computation speeds due to the calculation of numerous intermediate variables .
    • The BiLD loss notably enhances the overlap@8 metric while maintaining competitive overlap@1, indicating that it helps student logits align with the important parts of teacher logits more effectively than other methods .
    • By focusing on the key knowledge in teacher logits without introducing excessive hyperparameters, the BiLD loss offers a practical and straightforward approach to distillation for LLMs .

In summary, the Bi-directional Logits Difference (BiLD) loss stands out for its innovative approach to distilling large language models, addressing specific challenges related to logit distribution and ranking information, and demonstrating superior performance compared to existing distillation methods in both NLP and CV fields .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of large language model distillation. Noteworthy researchers in this area include Tushar Khot, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Xiao Cui, Yulei Qin, Marie-Catherine De Marneffe, Mandy Simons, Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh, Yao Fu, Hao Peng, Litu Ou, Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, William B Dolan, Yuxian Gu, Li Dong, Furu Wei, Minlie Huang, Gaurav Sahu, Olga Vechtomova, Dzmitry Bahdanau, Issam H Laradji, Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Shangquan Sun, Wenqi Ren, Jingzhi Li, Rui Wang, Xiaochun Cao, among others .

The key to the solution mentioned in the paper "BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation" is the proposal of the Bi-directional Logits Difference (BiLD) loss. This method filters out long-tail noise by utilizing only the top-k teacher and student logits, and leverages internal logits ranking information by constructing logits differences. The BiLD loss aims to address the challenges faced by existing logits distillation methods in effectively utilizing the internal ranking information from the logits, particularly in the context of large language models .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the proposed Bi-directional Logits Difference (BiLD) loss in distilling large language models (LLMs) . The experiments involved conducting comprehensive evaluations on 13 datasets using two types of LLMs . The results of the experiments showed that the BiLD loss outperformed supervised fine-tuning (SFT), vanilla KL loss, and five other distillation methods from both natural language processing (NLP) and computer vision (CV) fields . Additionally, the experiments demonstrated that the BiLD loss notably enhanced the overlap@8 metric while maintaining competitive overlap@1, indicating that the proposed loss helped align student logits with important parts of teacher logits .


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

The dataset used for quantitative evaluation in the study is the BLOOM and Qwen1.5 models, with BLOOM-7B and Qwen-4B being selected as teacher models, and BLOOM-3B and BLOOM-1B as student models . The code for the study will be made available soon .


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 the task-specific distillation of Large Language Models (LLMs) at the logit level and introduces the Bi-directional Logits Difference (BiLD) loss as a method to enhance distillation performance . The results demonstrate that the BiLD loss notably enhances overlap@8 while maintaining competitive overlap@1, indicating that it helps align student logits with important teacher logits . Additionally, the BiLD loss outperforms other distillation methods from both Natural Language Processing (NLP) and Computer Vision (CV) fields, showing superior effectiveness .

Moreover, the experimental results across all 13 datasets consistently show that the BiLD loss achieves the highest average accuracy compared to other methods tested, including supervised fine-tuning and vanilla KL loss . The study reports significant improvements in average accuracy with the BiLD loss in various distillation scenarios, highlighting its robust performance and effectiveness in knowledge distillation . The analysis of the effectiveness of clipping logits indicates that filtering out noise in the long-tail distribution of logits can enhance distillation performance, further supporting the efficacy of the BiLD loss .

Overall, the experiments conducted in the paper, along with the results and analysis presented, provide compelling evidence to support the scientific hypotheses related to the distillation of Large Language Models using the Bi-directional Logits Difference (BiLD) loss. The study's findings demonstrate the effectiveness and superiority of the BiLD loss in enhancing distillation performance and aligning student logits with important teacher logits, contributing significantly to the field of knowledge distillation for LLMs .


What are the contributions of this paper?

The paper "BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation" makes several key contributions in the field of knowledge distillation for large language models (LLMs) . The main contributions of this paper include:

  • Introduction of Bi-directional Logits Difference (BiLD) loss: The paper proposes the BiLD loss, which filters out long-tail noise in the logits of LLMs and leverages internal ranking information to improve distillation performance .
  • Enhanced distillation performance: Through comprehensive experiments on 13 datasets using two types of LLMs, the BiLD loss with only the top-8 logits outperformed other distillation methods from both natural language processing (NLP) and computer vision (CV) fields .
  • Improved alignment of student and teacher logits: The BiLD loss helps student logits align with the important parts of teacher logits, leading to better imitation of the teacher's primary behaviors at the logit level .
  • Addressing challenges: The paper acknowledges challenges such as computational complexity and loss of knowledge in the long-tail distribution of logits, highlighting avenues for future research to better utilize hidden knowledge in the long-tail distribution .

What work can be continued in depth?

Further research in the field of large language model distillation can be expanded by delving into the optimization objective proposed in the Bi-directional Logits Difference (BiLD) loss . This novel approach aims to enhance distillation performance by filtering out long-tail noise and leveraging internal logits ranking information . The BiLD loss has shown superior distillation performance using only the top-8 logits compared to other distillation methods, demonstrating its effectiveness in capturing key knowledge from the teacher model . Expanding on the study of the impact of temperature and the k value in the BiLD loss could provide valuable insights into further optimizing the distillation process .

Tables
3
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