A Critical Study of What Code-LLMs (Do Not) Learn

Abhinav Anand, Shweta Verma, Krishna Narasimhan, Mira Mezini·June 17, 2024

Summary

This study investigates the limitations of code language models (cLLMs) by analyzing attention maps and hidden representations. Key findings include: 1. cLLMs struggle to understand the connection between syntactic tokens and identifiers, with pre-trained models encoding these relations better than fine-tuned ones. 2. Larger models with billions of parameters do not necessarily encode more code-related information than smaller ones, and may even show a decline in encoding code syntax. 3. Attention maps are less effective in capturing syntactic-identifier relations, while syntactic-syntactic and identifier-identifier relations are better encoded. 4. Hidden representations lack sufficient information to differentiate identifier types and understand complex syntax, with issues persisting in larger models. 5. The study challenges previous assumptions about interpreting attention values and proposes a more nuanced approach to understand model internal representations. The research contributes to a better understanding of cLLM interpretability and highlights the need for novel training techniques or architectures to enhance code property encoding. Future work will expand the analysis to other programming languages and tasks.

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 understanding what code properties are encoded by Code Language Models (cLLMs) for prediction and generation, and which properties are not encoded by cLLMs . This problem is not new, but the paper contributes by systematically analyzing assumptions in previous work, highlighting misleading conclusions that can arise from these assumptions, and proposing new insights for the experimental setup of analyzing attention maps and hidden representations in cLLMs .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the interpretability of code-based Large Language Models (cLLMs) by conducting a systematic analysis of assumptions made in previous works and their potential impact on drawing misleading conclusions . The study focuses on evaluating the attention maps and hidden representations of cLLMs to determine their ability to encode various code relations among tokens, such as syntactic-syntactic, identifier-identifier, and syntactic-identifier relations, in order to identify specific relations that cLLMs may fail to encode accurately . Additionally, the paper investigates the encoding of code syntax, data-flow relations, and the discrimination between different identifier types by cLLMs, especially in the context of big models with increased parameters or fine-tuning on specific tasks .


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

The paper proposes two significant contributions to advance the interpretability of code-based Large Language Models (cLLMs) .

  1. Systematic Analysis of Assumptions: The paper conducts a thorough analysis of assumptions made in previous works, highlighting potential misleading conclusions. It examines the impact of attention thresholds and evaluation metrics on attention analysis . Specifically, it challenges previous studies that assumed an attention threshold of 0.3 and studied heads with the best precision, showing that these assumptions can lead to incorrect interpretations . The paper also questions the linear encoding of information in hidden representations, revealing that cLLMs struggle to encode syntactic relations and identifier types effectively .

  2. Fine-Grained Analysis of Attention and Hidden Representations: The paper delves into a detailed analysis of attention and hidden representations of cLLMs at the code token level to scrutinize what these models learn and do not learn . It distinguishes between different categories of code tokens, such as identifiers and syntactic tokens, to investigate specific relations that cLLMs may fail to encode . The study focuses on syntactic-syntactic, identifier-identifier, and syntactic-identifier relations encoded in self-attention values and hidden representations . Additionally, it extends the analysis to data-flow relations, creating a data flow graph to explore how values flow between variables .

Overall, the paper introduces a critical examination of the assumptions underlying previous works, along with a detailed investigation of attention and hidden representations in cLLMs to enhance the understanding of these models' capabilities and limitations in encoding code syntax and relations . The paper introduces novel insights and advancements compared to previous methods in the interpretability of code-based Large Language Models (cLLMs) . Here are the key characteristics and advantages of the proposed approach:

  1. Systematic Analysis of Assumptions:

    • The paper conducts a meticulous analysis of assumptions made in prior works, revealing potential misleading conclusions . It challenges the assumption of an attention threshold of 0.3 and the study of heads with the best precision, highlighting the impact of these assumptions on attention analysis .
    • Previous studies assumed a linear encoding of information in hidden representations, but the paper questions this assumption, showing that cLLMs struggle to encode syntactic relations effectively, especially in larger models .
  2. Fine-Grained Analysis of Attention and Hidden Representations:

    • The paper delves into a detailed examination of attention and hidden representations in cLLMs at the code token level, aiming to scrutinize what these models learn and do not learn .
    • It distinguishes between different categories of code tokens, such as identifiers and syntactic tokens, to investigate specific relations that cLLMs may fail to encode .
    • The study extends the analysis to data-flow relations, creating a data flow graph to explore how values flow between variables, providing a deeper understanding of the model's capabilities and limitations in encoding code syntax and relations .
  3. Contradictions with Previous Studies:

    • The paper challenges previous works that assumed a linear encoding of information in hidden representations, showing that hidden representations do not encode sufficient information to discriminate between different identifier types and understand subtle syntactical differences .
    • It contradicts previous studies that concluded hidden representations can encode syntactic relations among tokens, highlighting the limitations of cLLMs in encoding code syntax and data-flow relations, especially in larger models and fine-tuned settings .

Overall, the paper's systematic analysis of assumptions and fine-grained investigation of attention and hidden representations provide a critical perspective on the limitations and capabilities of cLLMs in understanding code syntax and relations, offering valuable insights for future research in enhancing the interpretability of these models .


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 code-LLMs. Noteworthy researchers in this area include Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen, Yonatan Belinkov, Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang, and many others . These researchers have contributed to advancing the interpretability of code-LLMs through various studies and analyses.

The key to the solution mentioned in the paper involves a systematic analysis of assumptions made in previous work to avoid misleading conclusions. The paper highlights the importance of examining the influence of attention thresholds and evaluation metrics on attention analysis, as well as the critical analysis of attention and hidden representations of code-LLMs at the code token level. By critically examining what these models learn and do not learn, the researchers aim to provide insights into the limitations of code-LLMs and suggest new experimental setups for analyzing attention maps and hidden representations .


How were the experiments in the paper designed?

The experiments in the paper were designed to analyze self-attention and hidden representations of code Language Models (cLLMs) . The experiments involved:

  • Comparing the self-attention of models with the motif structure in a program's Abstract Syntax Tree (AST) and Data Flow Graph (DFG) .
  • Probing hidden representations without classifiers using DirectProbe to evaluate the information encoded by the model in pairs of tokens .
  • Analyzing models with different architectures, pre-training objectives, and training datasets, ranging from 110M to 3.7B parameters .
  • Performing attention analysis to study the attention maps of cLLMs and hidden representation analysis to examine what the models learn and do not learn at the code token level .
  • Randomly sampling 3000 Python codes for the experiments, splitting them into train and test sets in an 80:20 ratio .
  • Providing statistics of the size and label of clusters created by DirectProbe for the last layer of some models and reporting the results of experiments with DirectProbe for middle and last layers of models not mentioned in the main text .
  • Evaluating the ability of cLLMs to encode syntactic-identifier relations, syntactic-syntactic relations, and identifier-identifier relations, as well as their performance in predicting edges in a DFG and sibling and distance prediction in an AST .

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

The dataset used for quantitative evaluation in the study is the CodeSearchNet (CSN) dataset, which consists of 2 million comment-code pairs from 6 programming languages: Go, Java, JavaScript, PHP, Python, and Ruby. The dataset is commonly used to pre-train models, and the Python codes used in the experiments were from the test split of CSN . The codes in the dataset are scrapped from GitHub and filtered to only contain codes with permissible licenses. The details of these licenses are available in the dataset .

Regarding the open-source nature of the code, the study does not explicitly mention whether the code used in the dataset is open source or not. However, since the codes are scraped from GitHub, it is likely that they are open source, as GitHub hosts a large number of open-source projects. It is important to note that the study focuses on analyzing the performance and behavior of various pre-trained language models on this dataset rather than the specific open-source status of the code .


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 needed verification. The study systematically analyzed assumptions made in previous works and highlighted misleading conclusions that can arise from incorrect assumptions . By examining the attention maps and hidden representations of code-LLMs, the study revealed shortcomings in encoding code syntax, especially with larger models and fine-tuning, which can impact the models' performance on real-world tasks . Additionally, the paper explored the relationships encoded by the models, such as syntactic-syntactic, identifier-identifier, and syntactic-identifier relations, shedding light on what the models learn and do not learn at a code token level . The experiments conducted with various models, architectures, and training objectives, ranging from encoder-only to decoder-only models, provided a comprehensive analysis of self-attention and hidden representations in code-LLMs . The study's detailed attention analysis, including the creation of syntax and data flow graphs, along with probing hidden representations without classifiers, contributed significantly to understanding the information encoded by the models and their limitations in capturing certain code properties .


What are the contributions of this paper?

The paper makes two significant contributions to enhance the interpretability of code-based Large Language Models (cLLMs) :

  1. Systematic Analysis of Assumptions: The paper conducts a systematic analysis of assumptions in previous work, highlighting that these assumptions can lead to misleading conclusions. It specifically examines the impact of attention thresholds and evaluation metrics on attention analysis in cLLMs.
  2. Identification of Encoding Issues: The study reveals that attention maps of cLLMs have limitations in encoding syntactic-identifier relations, while they are effective in encoding syntactic-syntactic and identifier-identifier relations. Additionally, the hidden representations of cLLMs lack the ability to discriminate between different identifier types and understand subtle syntactical differences.

What work can be continued in depth?

To further advance the understanding of code-LLMs, several areas of work can be continued in depth based on the provided context :

  • Designing more robust experiments to enhance the interpretability of code-LLMs.
  • Exploring novel training techniques and architectures to improve the models' ability to encode code properties instead of relying on larger models with memorization.
  • Investigating more recent instruction-tuned models by extending the study to NL-PL alignment for a comprehensive analysis.

Introduction
Background
Evolution of code language models (cLLMs)
Importance of understanding model behavior
Objective
To identify limitations of cLLMs
Investigate the impact of model size and pre-training on code understanding
Challenge existing interpretability assumptions
Method
Data Collection
Selection of diverse code datasets
Pre-trained and fine-tuned cLLM models
Data Preprocessing
Extraction of attention maps and hidden representations
Standardization and normalization of data
Attention Map Analysis
Attention to syntactic tokens vs identifiers
Comparison between pre-trained and fine-tuned models
Model size effect on syntactic-identifier relations
Attention map effectiveness in capturing different relations
Hidden Representation Analysis
Identifier type differentiation in hidden representations
Understanding of complex syntax in hidden space
Comparison across model sizes
Interpretation and Evaluation
Reevaluating attention values' significance
Proposing a nuanced approach to interpret internal representations
Findings
Limitations in cLLM understanding of syntax and identifiers
No clear advantage of larger models for code-related information
Attention map and hidden representation insights
Implications and Future Directions
Necessity of novel training techniques or architectures
Expansion to multiple programming languages and tasks
Research directions for enhanced cLLM interpretability
Conclusion
Summary of key findings
Significance of the study for the field
Recommendations for future research in code language models.
Basic info
papers
computation and language
software engineering
artificial intelligence
Advanced features
Insights
How do pre-trained and fine-tuned cLLMs differ in their ability to understand syntactic-token-identifier relations?
What are the primary limitations of code language models (cLLMs) as discussed in the study?
Does the size of the model have a consistent impact on code-related information encoding, as per the findings?
What are the implications of the study for future research on improving cLLM interpretability and code property encoding?

A Critical Study of What Code-LLMs (Do Not) Learn

Abhinav Anand, Shweta Verma, Krishna Narasimhan, Mira Mezini·June 17, 2024

Summary

This study investigates the limitations of code language models (cLLMs) by analyzing attention maps and hidden representations. Key findings include: 1. cLLMs struggle to understand the connection between syntactic tokens and identifiers, with pre-trained models encoding these relations better than fine-tuned ones. 2. Larger models with billions of parameters do not necessarily encode more code-related information than smaller ones, and may even show a decline in encoding code syntax. 3. Attention maps are less effective in capturing syntactic-identifier relations, while syntactic-syntactic and identifier-identifier relations are better encoded. 4. Hidden representations lack sufficient information to differentiate identifier types and understand complex syntax, with issues persisting in larger models. 5. The study challenges previous assumptions about interpreting attention values and proposes a more nuanced approach to understand model internal representations. The research contributes to a better understanding of cLLM interpretability and highlights the need for novel training techniques or architectures to enhance code property encoding. Future work will expand the analysis to other programming languages and tasks.
Mind map
Attention map effectiveness in capturing different relations
Model size effect on syntactic-identifier relations
Comparison across model sizes
Understanding of complex syntax in hidden space
Identifier type differentiation in hidden representations
Comparison between pre-trained and fine-tuned models
Attention to syntactic tokens vs identifiers
Proposing a nuanced approach to interpret internal representations
Reevaluating attention values' significance
Hidden Representation Analysis
Attention Map Analysis
Pre-trained and fine-tuned cLLM models
Selection of diverse code datasets
Challenge existing interpretability assumptions
Investigate the impact of model size and pre-training on code understanding
To identify limitations of cLLMs
Importance of understanding model behavior
Evolution of code language models (cLLMs)
Recommendations for future research in code language models.
Significance of the study for the field
Summary of key findings
Research directions for enhanced cLLM interpretability
Expansion to multiple programming languages and tasks
Necessity of novel training techniques or architectures
Attention map and hidden representation insights
No clear advantage of larger models for code-related information
Limitations in cLLM understanding of syntax and identifiers
Interpretation and Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Implications and Future Directions
Findings
Method
Introduction
Outline
Introduction
Background
Evolution of code language models (cLLMs)
Importance of understanding model behavior
Objective
To identify limitations of cLLMs
Investigate the impact of model size and pre-training on code understanding
Challenge existing interpretability assumptions
Method
Data Collection
Selection of diverse code datasets
Pre-trained and fine-tuned cLLM models
Data Preprocessing
Extraction of attention maps and hidden representations
Standardization and normalization of data
Attention Map Analysis
Attention to syntactic tokens vs identifiers
Comparison between pre-trained and fine-tuned models
Model size effect on syntactic-identifier relations
Attention map effectiveness in capturing different relations
Hidden Representation Analysis
Identifier type differentiation in hidden representations
Understanding of complex syntax in hidden space
Comparison across model sizes
Interpretation and Evaluation
Reevaluating attention values' significance
Proposing a nuanced approach to interpret internal representations
Findings
Limitations in cLLM understanding of syntax and identifiers
No clear advantage of larger models for code-related information
Attention map and hidden representation insights
Implications and Future Directions
Necessity of novel training techniques or architectures
Expansion to multiple programming languages and tasks
Research directions for enhanced cLLM interpretability
Conclusion
Summary of key findings
Significance of the study for the field
Recommendations for future research in code language models.
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 understanding what code properties are encoded by Code Language Models (cLLMs) for prediction and generation, and which properties are not encoded by cLLMs . This problem is not new, but the paper contributes by systematically analyzing assumptions in previous work, highlighting misleading conclusions that can arise from these assumptions, and proposing new insights for the experimental setup of analyzing attention maps and hidden representations in cLLMs .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the interpretability of code-based Large Language Models (cLLMs) by conducting a systematic analysis of assumptions made in previous works and their potential impact on drawing misleading conclusions . The study focuses on evaluating the attention maps and hidden representations of cLLMs to determine their ability to encode various code relations among tokens, such as syntactic-syntactic, identifier-identifier, and syntactic-identifier relations, in order to identify specific relations that cLLMs may fail to encode accurately . Additionally, the paper investigates the encoding of code syntax, data-flow relations, and the discrimination between different identifier types by cLLMs, especially in the context of big models with increased parameters or fine-tuning on specific tasks .


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

The paper proposes two significant contributions to advance the interpretability of code-based Large Language Models (cLLMs) .

  1. Systematic Analysis of Assumptions: The paper conducts a thorough analysis of assumptions made in previous works, highlighting potential misleading conclusions. It examines the impact of attention thresholds and evaluation metrics on attention analysis . Specifically, it challenges previous studies that assumed an attention threshold of 0.3 and studied heads with the best precision, showing that these assumptions can lead to incorrect interpretations . The paper also questions the linear encoding of information in hidden representations, revealing that cLLMs struggle to encode syntactic relations and identifier types effectively .

  2. Fine-Grained Analysis of Attention and Hidden Representations: The paper delves into a detailed analysis of attention and hidden representations of cLLMs at the code token level to scrutinize what these models learn and do not learn . It distinguishes between different categories of code tokens, such as identifiers and syntactic tokens, to investigate specific relations that cLLMs may fail to encode . The study focuses on syntactic-syntactic, identifier-identifier, and syntactic-identifier relations encoded in self-attention values and hidden representations . Additionally, it extends the analysis to data-flow relations, creating a data flow graph to explore how values flow between variables .

Overall, the paper introduces a critical examination of the assumptions underlying previous works, along with a detailed investigation of attention and hidden representations in cLLMs to enhance the understanding of these models' capabilities and limitations in encoding code syntax and relations . The paper introduces novel insights and advancements compared to previous methods in the interpretability of code-based Large Language Models (cLLMs) . Here are the key characteristics and advantages of the proposed approach:

  1. Systematic Analysis of Assumptions:

    • The paper conducts a meticulous analysis of assumptions made in prior works, revealing potential misleading conclusions . It challenges the assumption of an attention threshold of 0.3 and the study of heads with the best precision, highlighting the impact of these assumptions on attention analysis .
    • Previous studies assumed a linear encoding of information in hidden representations, but the paper questions this assumption, showing that cLLMs struggle to encode syntactic relations effectively, especially in larger models .
  2. Fine-Grained Analysis of Attention and Hidden Representations:

    • The paper delves into a detailed examination of attention and hidden representations in cLLMs at the code token level, aiming to scrutinize what these models learn and do not learn .
    • It distinguishes between different categories of code tokens, such as identifiers and syntactic tokens, to investigate specific relations that cLLMs may fail to encode .
    • The study extends the analysis to data-flow relations, creating a data flow graph to explore how values flow between variables, providing a deeper understanding of the model's capabilities and limitations in encoding code syntax and relations .
  3. Contradictions with Previous Studies:

    • The paper challenges previous works that assumed a linear encoding of information in hidden representations, showing that hidden representations do not encode sufficient information to discriminate between different identifier types and understand subtle syntactical differences .
    • It contradicts previous studies that concluded hidden representations can encode syntactic relations among tokens, highlighting the limitations of cLLMs in encoding code syntax and data-flow relations, especially in larger models and fine-tuned settings .

Overall, the paper's systematic analysis of assumptions and fine-grained investigation of attention and hidden representations provide a critical perspective on the limitations and capabilities of cLLMs in understanding code syntax and relations, offering valuable insights for future research in enhancing the interpretability of these models .


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 code-LLMs. Noteworthy researchers in this area include Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen, Yonatan Belinkov, Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang, and many others . These researchers have contributed to advancing the interpretability of code-LLMs through various studies and analyses.

The key to the solution mentioned in the paper involves a systematic analysis of assumptions made in previous work to avoid misleading conclusions. The paper highlights the importance of examining the influence of attention thresholds and evaluation metrics on attention analysis, as well as the critical analysis of attention and hidden representations of code-LLMs at the code token level. By critically examining what these models learn and do not learn, the researchers aim to provide insights into the limitations of code-LLMs and suggest new experimental setups for analyzing attention maps and hidden representations .


How were the experiments in the paper designed?

The experiments in the paper were designed to analyze self-attention and hidden representations of code Language Models (cLLMs) . The experiments involved:

  • Comparing the self-attention of models with the motif structure in a program's Abstract Syntax Tree (AST) and Data Flow Graph (DFG) .
  • Probing hidden representations without classifiers using DirectProbe to evaluate the information encoded by the model in pairs of tokens .
  • Analyzing models with different architectures, pre-training objectives, and training datasets, ranging from 110M to 3.7B parameters .
  • Performing attention analysis to study the attention maps of cLLMs and hidden representation analysis to examine what the models learn and do not learn at the code token level .
  • Randomly sampling 3000 Python codes for the experiments, splitting them into train and test sets in an 80:20 ratio .
  • Providing statistics of the size and label of clusters created by DirectProbe for the last layer of some models and reporting the results of experiments with DirectProbe for middle and last layers of models not mentioned in the main text .
  • Evaluating the ability of cLLMs to encode syntactic-identifier relations, syntactic-syntactic relations, and identifier-identifier relations, as well as their performance in predicting edges in a DFG and sibling and distance prediction in an AST .

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

The dataset used for quantitative evaluation in the study is the CodeSearchNet (CSN) dataset, which consists of 2 million comment-code pairs from 6 programming languages: Go, Java, JavaScript, PHP, Python, and Ruby. The dataset is commonly used to pre-train models, and the Python codes used in the experiments were from the test split of CSN . The codes in the dataset are scrapped from GitHub and filtered to only contain codes with permissible licenses. The details of these licenses are available in the dataset .

Regarding the open-source nature of the code, the study does not explicitly mention whether the code used in the dataset is open source or not. However, since the codes are scraped from GitHub, it is likely that they are open source, as GitHub hosts a large number of open-source projects. It is important to note that the study focuses on analyzing the performance and behavior of various pre-trained language models on this dataset rather than the specific open-source status of the code .


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 needed verification. The study systematically analyzed assumptions made in previous works and highlighted misleading conclusions that can arise from incorrect assumptions . By examining the attention maps and hidden representations of code-LLMs, the study revealed shortcomings in encoding code syntax, especially with larger models and fine-tuning, which can impact the models' performance on real-world tasks . Additionally, the paper explored the relationships encoded by the models, such as syntactic-syntactic, identifier-identifier, and syntactic-identifier relations, shedding light on what the models learn and do not learn at a code token level . The experiments conducted with various models, architectures, and training objectives, ranging from encoder-only to decoder-only models, provided a comprehensive analysis of self-attention and hidden representations in code-LLMs . The study's detailed attention analysis, including the creation of syntax and data flow graphs, along with probing hidden representations without classifiers, contributed significantly to understanding the information encoded by the models and their limitations in capturing certain code properties .


What are the contributions of this paper?

The paper makes two significant contributions to enhance the interpretability of code-based Large Language Models (cLLMs) :

  1. Systematic Analysis of Assumptions: The paper conducts a systematic analysis of assumptions in previous work, highlighting that these assumptions can lead to misleading conclusions. It specifically examines the impact of attention thresholds and evaluation metrics on attention analysis in cLLMs.
  2. Identification of Encoding Issues: The study reveals that attention maps of cLLMs have limitations in encoding syntactic-identifier relations, while they are effective in encoding syntactic-syntactic and identifier-identifier relations. Additionally, the hidden representations of cLLMs lack the ability to discriminate between different identifier types and understand subtle syntactical differences.

What work can be continued in depth?

To further advance the understanding of code-LLMs, several areas of work can be continued in depth based on the provided context :

  • Designing more robust experiments to enhance the interpretability of code-LLMs.
  • Exploring novel training techniques and architectures to improve the models' ability to encode code properties instead of relying on larger models with memorization.
  • Investigating more recent instruction-tuned models by extending the study to NL-PL alignment for a comprehensive analysis.
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