Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination

Luyao Shi, Michael Kazda, Bradley Sears, Nick Shropshire, Ruchir Puri·June 03, 2024

Summary

The paper introduces Ask-EDA, a chatbot designed to assist electronic design engineers by leveraging large language models, hybrid retrieval augmented generation (RAG), and abbreviation de-hallucination (ADH) techniques. It addresses LLM limitations by combining a hybrid search engine that combines dense and sparse retrieval, enabling it to provide up-to-date and accurate responses to design-related questions, commands, and abbreviation queries. Evaluation on three datasets (q2a-100, cmds-100, and abbr-100) demonstrates improved recall, with Granite-13b-chat-v2.1 and Llama2-13b-chat models showing varying performance depending on the dataset. The study highlights the potential of RAG for enhancing context and the need for future work, including refining retrieval models and incorporating reinforcement learning for better user alignment. Overall, Ask-EDA aims to boost engineers' productivity by providing a comprehensive and accurate NLP-based design assistant.

Key findings

1

Paper digest

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

The paper aims to address the challenge of efficiently locating relevant information for design engineers within large organizations, particularly focusing on the issue of redundant document versions and decentralized document storage . This problem becomes more critical for new employees or when new tools are introduced, leading to the need to understand new jargon and acronyms . The paper introduces Ask-EDA, a chat agent empowered by Large Language Models (LLMs), Hybrid Retrieval-Augmented Generation (RAG), and Abbreviation De-hallucination (ADH) techniques to enhance productivity for design engineers . While the challenge of redundant document versions and decentralized storage is not new, the approach of leveraging advanced technologies like LLMs, RAG, and ADH to address this issue represents a novel solution in the context of design engineering .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that utilizing a chat agent empowered by Large Language Models (LLMs), Hybrid Retrieval-Augmented Generation (RAG), and Abbreviation De-hallucination techniques can significantly enhance productivity among design engineers . The study demonstrates the effectiveness of Ask-EDA, the chat agent, across three distinct datasets focusing on general design question answering, design command answering, and abbreviation resolution . The research explores the integration of advanced technologies to provide relevant and accurate responses within the design domain, showcasing the potential benefits of leveraging these techniques in enhancing productivity and knowledge retrieval for design engineers .


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

The paper "Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination" proposes several innovative ideas, methods, and models to enhance the productivity of design engineers in Electronic Design Automation (EDA) . Here are the key contributions outlined in the paper:

  1. Large Language Models (LLMs): The paper leverages Large Language Models (LLMs) to provide natural language responses. Despite the impressive performance of LLMs, their responses are limited by outdated training data and the risk of incorporating confidential information. The paper aims to address these limitations by avoiding the inclusion of sensitive data and preventing incorrect responses through a phenomenon known as hallucination .

  2. Retrieval-Augmented Generation (RAG): The paper introduces Retrieval-Augmented Generation (RAG) as a method to combine information retrieval with system prompts to anchor LLMs to precise, current, and relevant information retrieved from external knowledge repositories. RAG aims to enhance the accuracy and relevance of responses provided by LLMs .

  3. Hybrid Search Engine: The paper develops a hybrid search engine that combines sentence transformers and BM25 to improve the accuracy and relevance of search results. By leveraging both dense and sparse retrieval algorithms, the hybrid search engine enhances the retrieval of relevant semantic context and specific technical terms, improving the overall search results .

  4. Abbreviation De-Hallucination (ADH) Component: To address the issue of hallucinated explanations for abbreviations by LLMs, the paper introduces an Abbreviation De-Hallucination (ADH) component. This component utilizes a pre-built abbreviation dictionary to provide relevant abbreviation knowledge to LLMs, ensuring accurate responses regarding abbreviations commonly used in the design space .

  5. Evaluation and Performance: The paper evaluates the performance of the proposed methods across three distinct datasets: q2a-100, cmds-100, and abbr-100. These datasets focus on general design question answering, design command answering, and abbreviation resolution, respectively. The results demonstrate the effectiveness of the proposed approaches in enhancing response quality and productivity for design engineers .

In conclusion, the paper introduces a comprehensive framework that integrates LLMs, Hybrid RAG, and Abbreviation De-Hallucination techniques to create Ask-EDA, a chat agent tailored to support design engineers in EDA. By leveraging these innovative methods, the paper aims to deliver more relevant, accurate, and context-aware responses to enhance productivity and efficiency in the design domain . The paper "Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG, and Abbreviation De-hallucination" introduces several innovative characteristics and advantages compared to previous methods in the domain of Electronic Design Automation (EDA) . Here are the key points highlighted in the paper:

  1. Retrieval-Augmented Generation (RAG): The paper demonstrates that utilizing RAG leads to significantly superior outcomes compared to not using RAG. Hybrid RAG, a combination of sparse and dense retrieval methods, exhibits noticeable enhancements over sparse-only and dense-only RAG approaches. The integration of RAG enhances the accuracy and relevance of responses provided by Large Language Models (LLMs) by anchoring them to precise, current, and relevant information retrieved from external knowledge repositories .

  2. Hybrid Search Engine: The paper introduces a hybrid search engine that combines sentence transformers and BM25 to improve the accuracy and relevance of search results. By leveraging both dense and sparse retrieval algorithms, the hybrid search engine enhances the retrieval of relevant semantic context and specific technical terms, thereby improving the overall search results .

  3. Abbreviation De-Hallucination (ADH) Component: To address the issue of hallucinated explanations for abbreviations by LLMs, the paper introduces an Abbreviation De-Hallucination (ADH) component. This component utilizes a pre-built abbreviation dictionary to provide relevant abbreviation knowledge to LLMs, ensuring accurate responses regarding abbreviations commonly used in the design space .

  4. Performance Evaluation: The paper evaluates the performance of the proposed methods across different datasets, including q2a-100, cmds-100, and abbr-100. The results demonstrate the effectiveness of the introduced characteristics in enhancing response quality and productivity for design engineers in EDA. The incorporation of hybrid RAG and ADH components notably enhances response quality, regardless of the LLM models employed, showcasing advancements over previous methods .

In conclusion, the characteristics and advantages of the proposed methods in the paper, such as Hybrid RAG, the hybrid search engine, and the Abbreviation De-Hallucination component, offer significant improvements in response accuracy, relevance, and productivity for design engineers in EDA compared to previous methods. These innovations aim to address the limitations of existing approaches and enhance the overall performance of design assistants in the EDA domain .


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

Several related research works exist in the field of design assistant empowered by LLM, Hybrid RAG, and Abbreviation De-hallucination. Noteworthy researchers in this field include J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. K¨uttler, M. Lewis, W.-t. Yih, T. Rockt¨aschel, N. Reimers, I. Gurevych, M. Liu, T.-D. Ene, R. Kirby, C. Cheng, N. Pinckney, R. Liang, J. Alben, H. Anand, S. Banerjee, I. Bayraktaroglu, G. V. Cormack, C. L. Clarke, S. Buettcher, W. Wang, F. Wei, L. Dong, among others .

The key to the solution mentioned in the paper involves leveraging Large Language Models (LLMs) for natural language responses, combining Retrieval-Augmented Generation (RAG) to anchor LLMs to precise and relevant information, utilizing Sentence Transformers for semantic information retrieval, and developing a hybrid search engine that combines dense and sparse retrieval algorithms to improve the accuracy and relevance of search results . Additionally, an Abbreviation De-Hallucination (ADH) component is employed to provide relevant abbreviation knowledge to LLMs, enhancing the accuracy of responses, especially in dealing with abbreviations commonly used in the design domain .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of Ask-EDA, a chat agent tailored to boost productivity among design engineers, by leveraging various techniques such as LLM, hybrid RAG, and abbreviation de-hallucination . The experiments assessed Ask-EDA's performance across three distinct datasets: q2a-100, cmds-100, and abbr-100, focusing on general design question answering, design command answering, and abbreviation resolution, respectively . The evaluation datasets were carefully curated to demonstrate the effectiveness of Ask-EDA in delivering relevant and accurate responses across diverse domains .


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

The dataset used for quantitative evaluation in the study is comprised of three subsets:

  • q2a-100 dataset: Contains 100 questions and answers extracted from a database for evaluation purposes .
  • cmds-100 dataset: Consists of 100 commands documented in manual pages for a construction and verification system, used as a test dataset .
  • abbr-100 dataset: Derived from an abbreviation dictionary, this subset includes 100 abbreviation terms with questions in the format "What does abbr stand for?" and their corresponding answers .

Regarding the code used in the study, some of the tools and models mentioned are open source:

  • Slack API: The Slack API used for building a natural language interface is open source .
  • LangChain: The LangChain tool used for document loading is open source .
  • Chroma: The Chroma tool mentioned in the study is open source .
  • IBM Research: Some IBM models and tools, such as Granite foundation models and Granite code models, are available online .

For specific details on the availability of the code used in the study, it is recommended to refer to the respective online sources provided in the references .


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

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study demonstrates the effectiveness of Ask-EDA, a chat agent tailored to enhance productivity for design engineers, by leveraging various techniques such as LLM, hybrid RAG, and abbreviation de-hallucination . The evaluation datasets, including q2a-100, cmds-100, and abbr-100, were curated to assess Ask-EDA's performance across different aspects of design question answering, design command answering, and abbreviation resolution . The results show that the incorporation of hybrid RAG and ADH components led to a notable enhancement in response quality, regardless of the LLM models used .

Furthermore, the study evaluated two LLMs, Granite-13b-chat-v2.1 and Llama2-13b-chat, and compared their performance in terms of F1 score and Recall on different datasets . The results indicated that employing RAG significantly improved the responses of both models, with hybrid retrieval achieving the highest performance . The experiments also demonstrated that the addition of the ADH component significantly boosted the performance on the abbr-100 dataset for both LLM models . This highlights the effectiveness of the abbreviation de-hallucination technique in improving response accuracy .

Overall, the experiments conducted in the paper, along with the analysis of the results, provide compelling evidence to support the scientific hypotheses underlying the development and evaluation of Ask-EDA, showcasing the efficacy of utilizing LLM, hybrid RAG, and abbreviation de-hallucination techniques in enhancing the performance of a design assistant chatbot for design engineers .


What are the contributions of this paper?

The paper "Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG, and Abbreviation De-hallucination" makes several significant contributions:

  • Introducing Ask-EDA, a chat agent tailored to enhance productivity for design engineers by leveraging Large Language Models (LLMs), hybrid Retrieval-Augmented Generation (RAG), and Abbreviation De-hallucination techniques .
  • Demonstrating the superiority of using RAG, particularly hybrid RAG, over not using RAG, and showcasing the enhancements of hybrid RAG over sparse-only and dense-only RAG .
  • Developing an Abbreviation De-Hallucination (ADH) component to address the issue of LLMs generating incorrect explanations for abbreviations, especially prevalent in the design space .
  • Curating and evaluating three distinct datasets (q2a-100, cmds-100, and abbr-100) to showcase the effectiveness of Ask-EDA across various domains, including general design question answering, design command answering, and abbreviation resolution .
  • Integrating the Slack API to create a user-friendly natural language interface for seamless interactions with the chat agent, enhancing the user experience and accessibility .
  • Exploring future directions such as fine-tuning more sophisticated sparse and dense retrieval models, extending fine-tuning on design data, and leveraging reinforcement learning from human feedback to align the chat agent more closely with human preferences .

What work can be continued in depth?

To further enhance the capabilities of Ask-EDA, several areas of work can be continued in depth based on the information provided in the document :

  • Fine-tuning Retrieval-Augmented Generation (RAG): One direction involves fine-tuning more sophisticated sparse and dense retrieval models to enhance RAG even further. This can lead to improved accuracy and relevance of search results by leveraging advanced retrieval techniques .
  • Extended Fine-tuning of Large Language Models (LLMs): Another aspect to explore is the extended fine-tuning of LLM models on design data. This process can help improve the performance of LLMs in providing accurate and relevant responses to design-related inquiries .
  • Reinforcement Learning from Human Feedback (RLHF): Leveraging reinforcement learning from human feedback can be a valuable technique to align the chat agent more closely with human preferences. By incorporating feedback data collected from interactions with users, the chat agent can be further refined to meet user expectations and improve response quality .
  • Exploration of Advanced Techniques: Additionally, exploring advanced techniques in information retrieval, natural language processing, and model training can contribute to enhancing the overall performance and capabilities of Ask-EDA. By staying updated with the latest advancements in the field, the chat agent can continue to evolve and provide more effective support to design engineers .

Introduction
Background
[ ] Emergence of large language models in technical support
[ ] Challenges faced by EDA professionals in finding relevant information
Objective
[ ] To develop a chatbot that leverages LLMs, RAG, and ADH for EDA assistance
[ ] Improve efficiency and accuracy in design-related queries
Method
Data Collection
[ ] Datasets: q2a-100, cmds-100, and abbr-100
[ ] Collection process and relevance to EDA domain
Data Preprocessing
[ ] Handling abbreviations and de-hallucination (ADH)
[ ] Cleaning and formatting design-related questions and commands
Hybrid Retrieval Augmented Generation (RAG)
[ ] Dense retrieval for contextually similar information
[ ] Sparse retrieval for specific and technical details
Model Selection and Evaluation
[ ] Granite-13b-chat-v2.1 and Llama2-13b-chat performance comparison
[ ] Recall metrics and dataset-specific analysis
Limitations and Future Work
[ ] refining retrieval models for improved accuracy
[ ] Reinforcement learning for better user interaction and alignment
[ ] User feedback and iterative improvement
Applications and Impact
Productivity Boost
[ ] Faster access to design resources
[ ] Time savings for engineers
EDA Industry Advancements
[ ] NLP-based support as a standard tool
[ ] Potential for broader adoption in technical assistance
Conclusion
[ ] Summary of Ask-EDA's achievements
[ ] Significance for the electronic design engineering community
[ ] Future directions and potential real-world implementation.
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What are the potential future directions for improving Ask-EDA, as mentioned in the paper?
What is Ask-EDA designed to do?
What evaluation datasets were used to assess the performance of Ask-EDA, and how did the Granite-13b-chat-v2.1 and Llama2-13b-chat models perform?
How does Ask-EDA address the limitations of large language models in the context of electronic design engineering?

Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination

Luyao Shi, Michael Kazda, Bradley Sears, Nick Shropshire, Ruchir Puri·June 03, 2024

Summary

The paper introduces Ask-EDA, a chatbot designed to assist electronic design engineers by leveraging large language models, hybrid retrieval augmented generation (RAG), and abbreviation de-hallucination (ADH) techniques. It addresses LLM limitations by combining a hybrid search engine that combines dense and sparse retrieval, enabling it to provide up-to-date and accurate responses to design-related questions, commands, and abbreviation queries. Evaluation on three datasets (q2a-100, cmds-100, and abbr-100) demonstrates improved recall, with Granite-13b-chat-v2.1 and Llama2-13b-chat models showing varying performance depending on the dataset. The study highlights the potential of RAG for enhancing context and the need for future work, including refining retrieval models and incorporating reinforcement learning for better user alignment. Overall, Ask-EDA aims to boost engineers' productivity by providing a comprehensive and accurate NLP-based design assistant.
Mind map
Potential for broader adoption in technical assistance
NLP-based support as a standard tool
Time savings for engineers
Faster access to design resources
User feedback and iterative improvement
Reinforcement learning for better user interaction and alignment
refining retrieval models for improved accuracy
Recall metrics and dataset-specific analysis
Granite-13b-chat-v2.1 and Llama2-13b-chat performance comparison
Sparse retrieval for specific and technical details
Dense retrieval for contextually similar information
Cleaning and formatting design-related questions and commands
Handling abbreviations and de-hallucination (ADH)
Collection process and relevance to EDA domain
Datasets: q2a-100, cmds-100, and abbr-100
Improve efficiency and accuracy in design-related queries
To develop a chatbot that leverages LLMs, RAG, and ADH for EDA assistance
Challenges faced by EDA professionals in finding relevant information
Emergence of large language models in technical support
Future directions and potential real-world implementation.
Significance for the electronic design engineering community
Summary of Ask-EDA's achievements
EDA Industry Advancements
Productivity Boost
Limitations and Future Work
Model Selection and Evaluation
Hybrid Retrieval Augmented Generation (RAG)
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Applications and Impact
Method
Introduction
Outline
Introduction
Background
[ ] Emergence of large language models in technical support
[ ] Challenges faced by EDA professionals in finding relevant information
Objective
[ ] To develop a chatbot that leverages LLMs, RAG, and ADH for EDA assistance
[ ] Improve efficiency and accuracy in design-related queries
Method
Data Collection
[ ] Datasets: q2a-100, cmds-100, and abbr-100
[ ] Collection process and relevance to EDA domain
Data Preprocessing
[ ] Handling abbreviations and de-hallucination (ADH)
[ ] Cleaning and formatting design-related questions and commands
Hybrid Retrieval Augmented Generation (RAG)
[ ] Dense retrieval for contextually similar information
[ ] Sparse retrieval for specific and technical details
Model Selection and Evaluation
[ ] Granite-13b-chat-v2.1 and Llama2-13b-chat performance comparison
[ ] Recall metrics and dataset-specific analysis
Limitations and Future Work
[ ] refining retrieval models for improved accuracy
[ ] Reinforcement learning for better user interaction and alignment
[ ] User feedback and iterative improvement
Applications and Impact
Productivity Boost
[ ] Faster access to design resources
[ ] Time savings for engineers
EDA Industry Advancements
[ ] NLP-based support as a standard tool
[ ] Potential for broader adoption in technical assistance
Conclusion
[ ] Summary of Ask-EDA's achievements
[ ] Significance for the electronic design engineering community
[ ] Future directions and potential real-world implementation.
Key findings
1

Paper digest

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

The paper aims to address the challenge of efficiently locating relevant information for design engineers within large organizations, particularly focusing on the issue of redundant document versions and decentralized document storage . This problem becomes more critical for new employees or when new tools are introduced, leading to the need to understand new jargon and acronyms . The paper introduces Ask-EDA, a chat agent empowered by Large Language Models (LLMs), Hybrid Retrieval-Augmented Generation (RAG), and Abbreviation De-hallucination (ADH) techniques to enhance productivity for design engineers . While the challenge of redundant document versions and decentralized storage is not new, the approach of leveraging advanced technologies like LLMs, RAG, and ADH to address this issue represents a novel solution in the context of design engineering .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that utilizing a chat agent empowered by Large Language Models (LLMs), Hybrid Retrieval-Augmented Generation (RAG), and Abbreviation De-hallucination techniques can significantly enhance productivity among design engineers . The study demonstrates the effectiveness of Ask-EDA, the chat agent, across three distinct datasets focusing on general design question answering, design command answering, and abbreviation resolution . The research explores the integration of advanced technologies to provide relevant and accurate responses within the design domain, showcasing the potential benefits of leveraging these techniques in enhancing productivity and knowledge retrieval for design engineers .


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

The paper "Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination" proposes several innovative ideas, methods, and models to enhance the productivity of design engineers in Electronic Design Automation (EDA) . Here are the key contributions outlined in the paper:

  1. Large Language Models (LLMs): The paper leverages Large Language Models (LLMs) to provide natural language responses. Despite the impressive performance of LLMs, their responses are limited by outdated training data and the risk of incorporating confidential information. The paper aims to address these limitations by avoiding the inclusion of sensitive data and preventing incorrect responses through a phenomenon known as hallucination .

  2. Retrieval-Augmented Generation (RAG): The paper introduces Retrieval-Augmented Generation (RAG) as a method to combine information retrieval with system prompts to anchor LLMs to precise, current, and relevant information retrieved from external knowledge repositories. RAG aims to enhance the accuracy and relevance of responses provided by LLMs .

  3. Hybrid Search Engine: The paper develops a hybrid search engine that combines sentence transformers and BM25 to improve the accuracy and relevance of search results. By leveraging both dense and sparse retrieval algorithms, the hybrid search engine enhances the retrieval of relevant semantic context and specific technical terms, improving the overall search results .

  4. Abbreviation De-Hallucination (ADH) Component: To address the issue of hallucinated explanations for abbreviations by LLMs, the paper introduces an Abbreviation De-Hallucination (ADH) component. This component utilizes a pre-built abbreviation dictionary to provide relevant abbreviation knowledge to LLMs, ensuring accurate responses regarding abbreviations commonly used in the design space .

  5. Evaluation and Performance: The paper evaluates the performance of the proposed methods across three distinct datasets: q2a-100, cmds-100, and abbr-100. These datasets focus on general design question answering, design command answering, and abbreviation resolution, respectively. The results demonstrate the effectiveness of the proposed approaches in enhancing response quality and productivity for design engineers .

In conclusion, the paper introduces a comprehensive framework that integrates LLMs, Hybrid RAG, and Abbreviation De-Hallucination techniques to create Ask-EDA, a chat agent tailored to support design engineers in EDA. By leveraging these innovative methods, the paper aims to deliver more relevant, accurate, and context-aware responses to enhance productivity and efficiency in the design domain . The paper "Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG, and Abbreviation De-hallucination" introduces several innovative characteristics and advantages compared to previous methods in the domain of Electronic Design Automation (EDA) . Here are the key points highlighted in the paper:

  1. Retrieval-Augmented Generation (RAG): The paper demonstrates that utilizing RAG leads to significantly superior outcomes compared to not using RAG. Hybrid RAG, a combination of sparse and dense retrieval methods, exhibits noticeable enhancements over sparse-only and dense-only RAG approaches. The integration of RAG enhances the accuracy and relevance of responses provided by Large Language Models (LLMs) by anchoring them to precise, current, and relevant information retrieved from external knowledge repositories .

  2. Hybrid Search Engine: The paper introduces a hybrid search engine that combines sentence transformers and BM25 to improve the accuracy and relevance of search results. By leveraging both dense and sparse retrieval algorithms, the hybrid search engine enhances the retrieval of relevant semantic context and specific technical terms, thereby improving the overall search results .

  3. Abbreviation De-Hallucination (ADH) Component: To address the issue of hallucinated explanations for abbreviations by LLMs, the paper introduces an Abbreviation De-Hallucination (ADH) component. This component utilizes a pre-built abbreviation dictionary to provide relevant abbreviation knowledge to LLMs, ensuring accurate responses regarding abbreviations commonly used in the design space .

  4. Performance Evaluation: The paper evaluates the performance of the proposed methods across different datasets, including q2a-100, cmds-100, and abbr-100. The results demonstrate the effectiveness of the introduced characteristics in enhancing response quality and productivity for design engineers in EDA. The incorporation of hybrid RAG and ADH components notably enhances response quality, regardless of the LLM models employed, showcasing advancements over previous methods .

In conclusion, the characteristics and advantages of the proposed methods in the paper, such as Hybrid RAG, the hybrid search engine, and the Abbreviation De-Hallucination component, offer significant improvements in response accuracy, relevance, and productivity for design engineers in EDA compared to previous methods. These innovations aim to address the limitations of existing approaches and enhance the overall performance of design assistants in the EDA domain .


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

Several related research works exist in the field of design assistant empowered by LLM, Hybrid RAG, and Abbreviation De-hallucination. Noteworthy researchers in this field include J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. K¨uttler, M. Lewis, W.-t. Yih, T. Rockt¨aschel, N. Reimers, I. Gurevych, M. Liu, T.-D. Ene, R. Kirby, C. Cheng, N. Pinckney, R. Liang, J. Alben, H. Anand, S. Banerjee, I. Bayraktaroglu, G. V. Cormack, C. L. Clarke, S. Buettcher, W. Wang, F. Wei, L. Dong, among others .

The key to the solution mentioned in the paper involves leveraging Large Language Models (LLMs) for natural language responses, combining Retrieval-Augmented Generation (RAG) to anchor LLMs to precise and relevant information, utilizing Sentence Transformers for semantic information retrieval, and developing a hybrid search engine that combines dense and sparse retrieval algorithms to improve the accuracy and relevance of search results . Additionally, an Abbreviation De-Hallucination (ADH) component is employed to provide relevant abbreviation knowledge to LLMs, enhancing the accuracy of responses, especially in dealing with abbreviations commonly used in the design domain .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of Ask-EDA, a chat agent tailored to boost productivity among design engineers, by leveraging various techniques such as LLM, hybrid RAG, and abbreviation de-hallucination . The experiments assessed Ask-EDA's performance across three distinct datasets: q2a-100, cmds-100, and abbr-100, focusing on general design question answering, design command answering, and abbreviation resolution, respectively . The evaluation datasets were carefully curated to demonstrate the effectiveness of Ask-EDA in delivering relevant and accurate responses across diverse domains .


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

The dataset used for quantitative evaluation in the study is comprised of three subsets:

  • q2a-100 dataset: Contains 100 questions and answers extracted from a database for evaluation purposes .
  • cmds-100 dataset: Consists of 100 commands documented in manual pages for a construction and verification system, used as a test dataset .
  • abbr-100 dataset: Derived from an abbreviation dictionary, this subset includes 100 abbreviation terms with questions in the format "What does abbr stand for?" and their corresponding answers .

Regarding the code used in the study, some of the tools and models mentioned are open source:

  • Slack API: The Slack API used for building a natural language interface is open source .
  • LangChain: The LangChain tool used for document loading is open source .
  • Chroma: The Chroma tool mentioned in the study is open source .
  • IBM Research: Some IBM models and tools, such as Granite foundation models and Granite code models, are available online .

For specific details on the availability of the code used in the study, it is recommended to refer to the respective online sources provided in the references .


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

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study demonstrates the effectiveness of Ask-EDA, a chat agent tailored to enhance productivity for design engineers, by leveraging various techniques such as LLM, hybrid RAG, and abbreviation de-hallucination . The evaluation datasets, including q2a-100, cmds-100, and abbr-100, were curated to assess Ask-EDA's performance across different aspects of design question answering, design command answering, and abbreviation resolution . The results show that the incorporation of hybrid RAG and ADH components led to a notable enhancement in response quality, regardless of the LLM models used .

Furthermore, the study evaluated two LLMs, Granite-13b-chat-v2.1 and Llama2-13b-chat, and compared their performance in terms of F1 score and Recall on different datasets . The results indicated that employing RAG significantly improved the responses of both models, with hybrid retrieval achieving the highest performance . The experiments also demonstrated that the addition of the ADH component significantly boosted the performance on the abbr-100 dataset for both LLM models . This highlights the effectiveness of the abbreviation de-hallucination technique in improving response accuracy .

Overall, the experiments conducted in the paper, along with the analysis of the results, provide compelling evidence to support the scientific hypotheses underlying the development and evaluation of Ask-EDA, showcasing the efficacy of utilizing LLM, hybrid RAG, and abbreviation de-hallucination techniques in enhancing the performance of a design assistant chatbot for design engineers .


What are the contributions of this paper?

The paper "Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG, and Abbreviation De-hallucination" makes several significant contributions:

  • Introducing Ask-EDA, a chat agent tailored to enhance productivity for design engineers by leveraging Large Language Models (LLMs), hybrid Retrieval-Augmented Generation (RAG), and Abbreviation De-hallucination techniques .
  • Demonstrating the superiority of using RAG, particularly hybrid RAG, over not using RAG, and showcasing the enhancements of hybrid RAG over sparse-only and dense-only RAG .
  • Developing an Abbreviation De-Hallucination (ADH) component to address the issue of LLMs generating incorrect explanations for abbreviations, especially prevalent in the design space .
  • Curating and evaluating three distinct datasets (q2a-100, cmds-100, and abbr-100) to showcase the effectiveness of Ask-EDA across various domains, including general design question answering, design command answering, and abbreviation resolution .
  • Integrating the Slack API to create a user-friendly natural language interface for seamless interactions with the chat agent, enhancing the user experience and accessibility .
  • Exploring future directions such as fine-tuning more sophisticated sparse and dense retrieval models, extending fine-tuning on design data, and leveraging reinforcement learning from human feedback to align the chat agent more closely with human preferences .

What work can be continued in depth?

To further enhance the capabilities of Ask-EDA, several areas of work can be continued in depth based on the information provided in the document :

  • Fine-tuning Retrieval-Augmented Generation (RAG): One direction involves fine-tuning more sophisticated sparse and dense retrieval models to enhance RAG even further. This can lead to improved accuracy and relevance of search results by leveraging advanced retrieval techniques .
  • Extended Fine-tuning of Large Language Models (LLMs): Another aspect to explore is the extended fine-tuning of LLM models on design data. This process can help improve the performance of LLMs in providing accurate and relevant responses to design-related inquiries .
  • Reinforcement Learning from Human Feedback (RLHF): Leveraging reinforcement learning from human feedback can be a valuable technique to align the chat agent more closely with human preferences. By incorporating feedback data collected from interactions with users, the chat agent can be further refined to meet user expectations and improve response quality .
  • Exploration of Advanced Techniques: Additionally, exploring advanced techniques in information retrieval, natural language processing, and model training can contribute to enhancing the overall performance and capabilities of Ask-EDA. By staying updated with the latest advancements in the field, the chat agent can continue to evolve and provide more effective support to design engineers .
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