Token-based Decision Criteria Are Suboptimal in In-context Learning

Hakaze Cho, Yoshihiro Sakai, Mariko Kato, Kenshiro Tanaka, Akira Ishii, Naoya Inoue·June 24, 2024

Summary

This paper investigates the limitations of token-based classification in In-Context Learning (ICL) for language models, particularly in terms of biases and under-calibration. The authors propose Hidden Calibration, which replaces token probabilities with nearest centroid classification on hidden states, improving performance by about 20% across 10 datasets. Hidden Calibration leverages the linear separability of language models' hidden representations and outperforms token-based methods, even with minimal computational cost. The study demonstrates that hidden state-based approaches, like Hidden Calibration, yield better classification criteria with lower inter-category overlap, and that the method benefits from demonstrations, enhancing linear separability and transferability across tasks with the same label space. The paper also suggests future research on automatic token selection and combining Hidden Calibration with other calibration techniques.

Key findings

8

Paper digest

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

The paper aims to address the issue of using token-based decision criteria in in-context learning (ICL) and proposes a solution called Hidden Calibration to overcome the limitations of existing calibration practices . This problem is not entirely new, as previous works have focused on using manually selected labels as projecting subspaces for classification criteria, which have been found to be under-guaranteed . The paper introduces Hidden Calibration as a new state-of-the-art method to eliminate unreliable human intuition from ICL prediction decoding and improve classification criteria by calculating centroids on hidden states instead of relying on human-selected token-based probabilities .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that using token probability as the classification criteria in In-context Learning (ICL) methods is suboptimal. Instead, the paper proposes a new approach called Hidden Calibration to address this issue by discarding token-based classification criteria and focusing on the hidden state for better classification criteria . The experiments conducted in the paper demonstrate that Hidden Calibration leads to improved performance by reducing inter-category overlap and promoting hidden state convergence through demonstrations, thus enhancing the principle of performance improvement in ICL .


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

The paper proposes a novel approach called Hidden Calibration to address the common drawback of using token probability as the classification criteria in in-context learning . This method aims to discard token-based classification criteria and instead focuses on the hidden state to create better criteria by reducing inter-category overlap . Hidden Calibration utilizes a calibration dataset to calculate centroids and decode the hidden state, which leads to improved performance by establishing a single linear classification boundary . The paper suggests that exploring the hidden state rather than token probabilities can enhance the understanding and effectiveness of in-context learning calibration . The proposed method, Hidden Calibration, offers several key characteristics and advantages compared to previous methods in in-context learning .

  1. Hidden State Focus: Hidden Calibration shifts the focus from token probability to the hidden state, aiming to create better classification criteria by reducing inter-category overlap . This approach leads to improved performance by establishing a single linear classification boundary .

  2. Reduced Overlaps: Hidden Calibration demonstrates significantly lower overlaps compared to token-based methods, indicating better classification criteria and improved performance potential . The method finds criteria with smaller overlap, enhancing the effectiveness of classification boundaries .

  3. Efficiency and Usability: Hidden Calibration is highlighted for its efficiency and usability, avoiding the overhead associated with other methods based on token probability . This makes it more practical and elegant for implementation in in-context learning scenarios .

  4. State-of-the-Art Performance: Experimental comparisons have shown that Hidden Calibration represents a new state-of-the-art in in-context learning, outperforming token-based methods . The method's ability to reduce inter-category overlap contributes to its superior performance in classification tasks .

  5. Linear Separability: Hidden Calibration utilizes simple linear boundaries to classify in-context learning examples effectively . The method leverages the linear separability of hidden states produced by language models, further enhanced by demonstrations, to facilitate the classification process .

  6. Calibration Dataset Usage: Hidden Calibration leverages a calibration dataset to calculate centroids and decode the hidden state, leading to improved performance and more accurate classification criteria . Even with one example per category, Hidden Calibration has been shown to enhance the performance of in-context learning .

In summary, Hidden Calibration introduces a novel approach that focuses on the hidden state, reduces overlaps, improves classification criteria, and enhances the efficiency and performance of in-context learning compared to traditional token-based methods .


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 in-context learning have been identified:

  • Noteworthy researchers in this field include Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman, Yizhong Wang, Swaroop Mishra, Pegah Alipoor-molabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, Quoc V Le, Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Benfeng Xu, Quan Wang, Zhendong Mao, Yajuan Lyu, Qiaoqiao She, Yongdong Zhang, Kurt Hornik, Maxwell Stinchcombe, Halbert White, Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi, Srinivasan Iyer, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Tianlu Wang, Qing Liu, Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jun Zhao, Kang Liu, P. Malo, A. Sinha, P. Korhonen, J. Wallenius, P. Takala, Sewon Min, Mike Lewis, Luke Zettlemoyer, Xinxi Lyu, Ari Holtzman, Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Punit Singh Koura, Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh, Xiang Zhang, Junbo Jake Zhao, Yann LeCun, Yufeng Zhao, Yoshihiro Sakai, Naoya Inoue, Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine Heller, Subhrajit Roy, Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen, Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, Manuela Sanguinetti .
  • The key solution mentioned in the paper is the proposal of Hidden Calibration as a new state-of-the-art approach to address the limitations of previous calibration practices in in-context learning. Hidden Calibration eliminates unreliable human intuition from prediction decoding by using a nearest centroid classifier on hidden states instead of human-selected token-based probabilities. This method aims to find better classification criteria with less inter-categories overlap, leveraging linearly separable clusters provided by language models with the assistance of demonstrations .

How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on two main categories of methods for enhancing in-context learning . The first category involves model parameter update-based methods, which aim to bridge the gap between the in-context learning (ICL) objective and causal language modeling objective. These methods typically rely on supervised fine-tuning, self-supervised training, and non-gradient methods, requiring significant computational resources and data overhead to update large language model parameters .

On the other hand, the second category of methods focuses on classification criteria-based approaches, specifically calibration methods. These methods aim to adjust the output label probabilities without modifying the main feed-forward calculation processes and their parameters. The motivation behind these calibration methods is to eliminate prior bias and unfaithful confidence in ICL by calibrating the output label probabilities .

The experimental design involved using 10 datasets for evaluation, with a random split into calibration and test sets for each dataset. The calibration sets were used to build prompt examples and test for performance, ensuring a comprehensive evaluation of the proposed methods . The experiments also included visualization techniques, such as plotting curves against the number of demonstrations to observe in-context learning dynamics and linear separability of hidden states .

Overall, the experiments in the paper were meticulously designed to compare the effectiveness of Hidden Calibration with token-based methods, analyze the impact of different calibration criteria, and provide insights into the principles underlying the performance improvements achieved by Hidden Calibration in in-context learning .


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

The dataset used for quantitative evaluation in the study is called OPT-2.7B . The code for the study is open source as it mentions "arXiv preprint" which indicates that the paper is publicly available on arXiv, a platform for open access to scientific papers .


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

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study delves into the effectiveness of token-based decision criteria in in-context learning (ICL) and the calibration methods used to adjust predicted logits for more accurate predictions . The research highlights the limitations of relying on human intuition for label token selection and emphasizes the need for automatic optimal label token selection in prompts to enhance ICL performance . Additionally, the paper explores the combination of Hidden Calibration with other probability calibrations to further improve performance, indicating avenues for future research .

Moreover, the study investigates the transferability of centroids among various datasets with the same label space, revealing significant differences in hidden state distributions even when datasets share a common label space . This analysis raises doubts about the reliability of utilizing fixed token un-embedding vectors to decode classification criteria, emphasizing the need for more robust and adaptable methods in ICL . The experiments conducted on various k values and datasets provide valuable insights into the convergence of hidden states and the aggregation trends within and between categories, shedding light on the underlying principles of ICL and traditional calibrations .

Furthermore, the comparison to previous works, such as probe methods, showcases the advantages of the Hidden Calibration approach in terms of user-friendliness, efficiency, and effectiveness in improving classification performance without the need for gradient-based training . The study's detailed analysis of the overlap calculation, in-context learning dynamics, and efficiency of Hidden Calibration with varying calibration set sizes contributes to a comprehensive understanding of the principles and implications of token-based decision criteria in ICL .


What are the contributions of this paper?

The contributions of the paper "Token-based Decision Criteria Are Suboptimal in In-context Learning" include:

  • Exploring Two Categories of Methods: The paper delves into two main categories of methods focused on enhancing in-context learning. These categories are model parameter update-based methods and classification criteria-based methods .
  • Model Parameter Update-based Methods: It discusses methods that involve supervised fine-tuning, self-supervised training, and non-gradient methods to bridge the gap between in-context learning objectives and causal language modeling objectives .
  • Classification Criteria-based Methods: The paper highlights methods that focus on calibrating output label probabilities to eliminate bias and enhance confidence in in-context learning. These methods aim to improve the fidelity of in-context learning by adjusting output category logits without modifying the main feed-forward calculation processes .
  • Experimental Details: The paper provides insights into the datasets used for experimentation, the calibration sets, and test sets created for evaluation purposes. It outlines the process of splitting the datasets into calibration and test sets for conducting experiments on in-context learning .
  • Comparative Analysis: The paper conducts a comparative analysis of different methods and their impact on in-context learning, shedding light on the effectiveness of model parameter update-based and classification criteria-based approaches in enhancing the performance of language models in in-context learning scenarios .

What work can be continued in depth?

Further research in the field of in-context learning can be expanded in several directions based on the existing work:

  • Automatic Label Token Selection: Exploring methods to automatically select optimal label tokens in prompts could be a crucial area for future research to enhance the performance of in-context learning .
  • Combining Probability Calibrations: Investigating the combination of other probability calibrations with Hidden Calibration for improved performance could be a promising avenue for further research .
  • Theoretical and Experimental Analysis: Conducting more in-depth theoretical and experimental analysis, especially focusing on understanding how to enhance intra-category aggregation and the reasons behind the occurrence or absence of such aggregation, would be beneficial for advancing the principles of in-context learning .
  • Transferability Studies: Further exploring the transferability of centroids among various datasets and different values of k could provide insights into the robustness and generalizability of in-context learning models .
  • Comparison to Previous Works: Continuously comparing the proposed methods to existing probe methods and highlighting the advantages and differences can contribute to a better understanding of the effectiveness and efficiency of different approaches in in-context learning .

Tables

2

Introduction
Background
[1] Token-based classification in ICL limitations
[2] Biases and under-calibration issues
Objective
[3] To propose Hidden Calibration as a solution
[4] Aim for 20% performance improvement across 10 datasets
Method
Data Collection
[5] Selection of 10 diverse datasets for evaluation
Data Preprocessing
[6] Analysis of token probabilities and hidden states
[7] Identifying linear separability in hidden representations
Hidden Calibration Approach
[8] Replacing token probabilities with nearest centroid classification
[9] Minimal computational cost
[10] Leveraging linear separability
Performance Evaluation
[11] Comparison with token-based methods
[12] Better classification criteria and lower inter-category overlap
Transferability and Demonstrations
[13] Enhanced linear separability with demonstrations
[14] Transferability across tasks with same label space
Future Research Directions
[15] Automatic token selection
[16] Combining Hidden Calibration with other calibration techniques
Results and Discussion
[17] Improved performance statistics
[18] Case studies and real-world implications
Conclusion
[19] Summary of findings and contributions
[20] Implications for the field of language model calibration and ICL
References
[21] Cited works and literature review
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What is the main advantage of Hidden Calibration over token-based methods, as mentioned in the study?
What does the paper focus on in the context of In-Context Learning for language models?
How does the use of demonstrations affect the performance of Hidden Calibration, according to the paper?
How much improvement does Hidden Calibration achieve compared to token-based classification?

Token-based Decision Criteria Are Suboptimal in In-context Learning

Hakaze Cho, Yoshihiro Sakai, Mariko Kato, Kenshiro Tanaka, Akira Ishii, Naoya Inoue·June 24, 2024

Summary

This paper investigates the limitations of token-based classification in In-Context Learning (ICL) for language models, particularly in terms of biases and under-calibration. The authors propose Hidden Calibration, which replaces token probabilities with nearest centroid classification on hidden states, improving performance by about 20% across 10 datasets. Hidden Calibration leverages the linear separability of language models' hidden representations and outperforms token-based methods, even with minimal computational cost. The study demonstrates that hidden state-based approaches, like Hidden Calibration, yield better classification criteria with lower inter-category overlap, and that the method benefits from demonstrations, enhancing linear separability and transferability across tasks with the same label space. The paper also suggests future research on automatic token selection and combining Hidden Calibration with other calibration techniques.
Mind map
[14] Transferability across tasks with same label space
[13] Enhanced linear separability with demonstrations
[10] Leveraging linear separability
[9] Minimal computational cost
[8] Replacing token probabilities with nearest centroid classification
[16] Combining Hidden Calibration with other calibration techniques
[15] Automatic token selection
Transferability and Demonstrations
Hidden Calibration Approach
[5] Selection of 10 diverse datasets for evaluation
[4] Aim for 20% performance improvement across 10 datasets
[3] To propose Hidden Calibration as a solution
[2] Biases and under-calibration issues
[1] Token-based classification in ICL limitations
[21] Cited works and literature review
[20] Implications for the field of language model calibration and ICL
[19] Summary of findings and contributions
[18] Case studies and real-world implications
[17] Improved performance statistics
Future Research Directions
Performance Evaluation
Data Preprocessing
Data Collection
Objective
Background
References
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
[1] Token-based classification in ICL limitations
[2] Biases and under-calibration issues
Objective
[3] To propose Hidden Calibration as a solution
[4] Aim for 20% performance improvement across 10 datasets
Method
Data Collection
[5] Selection of 10 diverse datasets for evaluation
Data Preprocessing
[6] Analysis of token probabilities and hidden states
[7] Identifying linear separability in hidden representations
Hidden Calibration Approach
[8] Replacing token probabilities with nearest centroid classification
[9] Minimal computational cost
[10] Leveraging linear separability
Performance Evaluation
[11] Comparison with token-based methods
[12] Better classification criteria and lower inter-category overlap
Transferability and Demonstrations
[13] Enhanced linear separability with demonstrations
[14] Transferability across tasks with same label space
Future Research Directions
[15] Automatic token selection
[16] Combining Hidden Calibration with other calibration techniques
Results and Discussion
[17] Improved performance statistics
[18] Case studies and real-world implications
Conclusion
[19] Summary of findings and contributions
[20] Implications for the field of language model calibration and ICL
References
[21] Cited works and literature review
Key findings
8

Paper digest

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

The paper aims to address the issue of using token-based decision criteria in in-context learning (ICL) and proposes a solution called Hidden Calibration to overcome the limitations of existing calibration practices . This problem is not entirely new, as previous works have focused on using manually selected labels as projecting subspaces for classification criteria, which have been found to be under-guaranteed . The paper introduces Hidden Calibration as a new state-of-the-art method to eliminate unreliable human intuition from ICL prediction decoding and improve classification criteria by calculating centroids on hidden states instead of relying on human-selected token-based probabilities .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that using token probability as the classification criteria in In-context Learning (ICL) methods is suboptimal. Instead, the paper proposes a new approach called Hidden Calibration to address this issue by discarding token-based classification criteria and focusing on the hidden state for better classification criteria . The experiments conducted in the paper demonstrate that Hidden Calibration leads to improved performance by reducing inter-category overlap and promoting hidden state convergence through demonstrations, thus enhancing the principle of performance improvement in ICL .


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

The paper proposes a novel approach called Hidden Calibration to address the common drawback of using token probability as the classification criteria in in-context learning . This method aims to discard token-based classification criteria and instead focuses on the hidden state to create better criteria by reducing inter-category overlap . Hidden Calibration utilizes a calibration dataset to calculate centroids and decode the hidden state, which leads to improved performance by establishing a single linear classification boundary . The paper suggests that exploring the hidden state rather than token probabilities can enhance the understanding and effectiveness of in-context learning calibration . The proposed method, Hidden Calibration, offers several key characteristics and advantages compared to previous methods in in-context learning .

  1. Hidden State Focus: Hidden Calibration shifts the focus from token probability to the hidden state, aiming to create better classification criteria by reducing inter-category overlap . This approach leads to improved performance by establishing a single linear classification boundary .

  2. Reduced Overlaps: Hidden Calibration demonstrates significantly lower overlaps compared to token-based methods, indicating better classification criteria and improved performance potential . The method finds criteria with smaller overlap, enhancing the effectiveness of classification boundaries .

  3. Efficiency and Usability: Hidden Calibration is highlighted for its efficiency and usability, avoiding the overhead associated with other methods based on token probability . This makes it more practical and elegant for implementation in in-context learning scenarios .

  4. State-of-the-Art Performance: Experimental comparisons have shown that Hidden Calibration represents a new state-of-the-art in in-context learning, outperforming token-based methods . The method's ability to reduce inter-category overlap contributes to its superior performance in classification tasks .

  5. Linear Separability: Hidden Calibration utilizes simple linear boundaries to classify in-context learning examples effectively . The method leverages the linear separability of hidden states produced by language models, further enhanced by demonstrations, to facilitate the classification process .

  6. Calibration Dataset Usage: Hidden Calibration leverages a calibration dataset to calculate centroids and decode the hidden state, leading to improved performance and more accurate classification criteria . Even with one example per category, Hidden Calibration has been shown to enhance the performance of in-context learning .

In summary, Hidden Calibration introduces a novel approach that focuses on the hidden state, reduces overlaps, improves classification criteria, and enhances the efficiency and performance of in-context learning compared to traditional token-based methods .


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 in-context learning have been identified:

  • Noteworthy researchers in this field include Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman, Yizhong Wang, Swaroop Mishra, Pegah Alipoor-molabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, Quoc V Le, Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Benfeng Xu, Quan Wang, Zhendong Mao, Yajuan Lyu, Qiaoqiao She, Yongdong Zhang, Kurt Hornik, Maxwell Stinchcombe, Halbert White, Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi, Srinivasan Iyer, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Tianlu Wang, Qing Liu, Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jun Zhao, Kang Liu, P. Malo, A. Sinha, P. Korhonen, J. Wallenius, P. Takala, Sewon Min, Mike Lewis, Luke Zettlemoyer, Xinxi Lyu, Ari Holtzman, Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Punit Singh Koura, Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh, Xiang Zhang, Junbo Jake Zhao, Yann LeCun, Yufeng Zhao, Yoshihiro Sakai, Naoya Inoue, Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine Heller, Subhrajit Roy, Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen, Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, Manuela Sanguinetti .
  • The key solution mentioned in the paper is the proposal of Hidden Calibration as a new state-of-the-art approach to address the limitations of previous calibration practices in in-context learning. Hidden Calibration eliminates unreliable human intuition from prediction decoding by using a nearest centroid classifier on hidden states instead of human-selected token-based probabilities. This method aims to find better classification criteria with less inter-categories overlap, leveraging linearly separable clusters provided by language models with the assistance of demonstrations .

How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on two main categories of methods for enhancing in-context learning . The first category involves model parameter update-based methods, which aim to bridge the gap between the in-context learning (ICL) objective and causal language modeling objective. These methods typically rely on supervised fine-tuning, self-supervised training, and non-gradient methods, requiring significant computational resources and data overhead to update large language model parameters .

On the other hand, the second category of methods focuses on classification criteria-based approaches, specifically calibration methods. These methods aim to adjust the output label probabilities without modifying the main feed-forward calculation processes and their parameters. The motivation behind these calibration methods is to eliminate prior bias and unfaithful confidence in ICL by calibrating the output label probabilities .

The experimental design involved using 10 datasets for evaluation, with a random split into calibration and test sets for each dataset. The calibration sets were used to build prompt examples and test for performance, ensuring a comprehensive evaluation of the proposed methods . The experiments also included visualization techniques, such as plotting curves against the number of demonstrations to observe in-context learning dynamics and linear separability of hidden states .

Overall, the experiments in the paper were meticulously designed to compare the effectiveness of Hidden Calibration with token-based methods, analyze the impact of different calibration criteria, and provide insights into the principles underlying the performance improvements achieved by Hidden Calibration in in-context learning .


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

The dataset used for quantitative evaluation in the study is called OPT-2.7B . The code for the study is open source as it mentions "arXiv preprint" which indicates that the paper is publicly available on arXiv, a platform for open access to scientific papers .


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

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study delves into the effectiveness of token-based decision criteria in in-context learning (ICL) and the calibration methods used to adjust predicted logits for more accurate predictions . The research highlights the limitations of relying on human intuition for label token selection and emphasizes the need for automatic optimal label token selection in prompts to enhance ICL performance . Additionally, the paper explores the combination of Hidden Calibration with other probability calibrations to further improve performance, indicating avenues for future research .

Moreover, the study investigates the transferability of centroids among various datasets with the same label space, revealing significant differences in hidden state distributions even when datasets share a common label space . This analysis raises doubts about the reliability of utilizing fixed token un-embedding vectors to decode classification criteria, emphasizing the need for more robust and adaptable methods in ICL . The experiments conducted on various k values and datasets provide valuable insights into the convergence of hidden states and the aggregation trends within and between categories, shedding light on the underlying principles of ICL and traditional calibrations .

Furthermore, the comparison to previous works, such as probe methods, showcases the advantages of the Hidden Calibration approach in terms of user-friendliness, efficiency, and effectiveness in improving classification performance without the need for gradient-based training . The study's detailed analysis of the overlap calculation, in-context learning dynamics, and efficiency of Hidden Calibration with varying calibration set sizes contributes to a comprehensive understanding of the principles and implications of token-based decision criteria in ICL .


What are the contributions of this paper?

The contributions of the paper "Token-based Decision Criteria Are Suboptimal in In-context Learning" include:

  • Exploring Two Categories of Methods: The paper delves into two main categories of methods focused on enhancing in-context learning. These categories are model parameter update-based methods and classification criteria-based methods .
  • Model Parameter Update-based Methods: It discusses methods that involve supervised fine-tuning, self-supervised training, and non-gradient methods to bridge the gap between in-context learning objectives and causal language modeling objectives .
  • Classification Criteria-based Methods: The paper highlights methods that focus on calibrating output label probabilities to eliminate bias and enhance confidence in in-context learning. These methods aim to improve the fidelity of in-context learning by adjusting output category logits without modifying the main feed-forward calculation processes .
  • Experimental Details: The paper provides insights into the datasets used for experimentation, the calibration sets, and test sets created for evaluation purposes. It outlines the process of splitting the datasets into calibration and test sets for conducting experiments on in-context learning .
  • Comparative Analysis: The paper conducts a comparative analysis of different methods and their impact on in-context learning, shedding light on the effectiveness of model parameter update-based and classification criteria-based approaches in enhancing the performance of language models in in-context learning scenarios .

What work can be continued in depth?

Further research in the field of in-context learning can be expanded in several directions based on the existing work:

  • Automatic Label Token Selection: Exploring methods to automatically select optimal label tokens in prompts could be a crucial area for future research to enhance the performance of in-context learning .
  • Combining Probability Calibrations: Investigating the combination of other probability calibrations with Hidden Calibration for improved performance could be a promising avenue for further research .
  • Theoretical and Experimental Analysis: Conducting more in-depth theoretical and experimental analysis, especially focusing on understanding how to enhance intra-category aggregation and the reasons behind the occurrence or absence of such aggregation, would be beneficial for advancing the principles of in-context learning .
  • Transferability Studies: Further exploring the transferability of centroids among various datasets and different values of k could provide insights into the robustness and generalizability of in-context learning models .
  • Comparison to Previous Works: Continuously comparing the proposed methods to existing probe methods and highlighting the advantages and differences can contribute to a better understanding of the effectiveness and efficiency of different approaches in in-context learning .
Tables
2
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