Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models

Akchay Srivastava, Atif Memon·June 19, 2024

Summary

This study presents a comprehensive taxonomy of 52 open domain question answering (ODQA) datasets and 20 evaluation metrics, covering textual and multimodal modalities. It differentiates datasets based on modality, question difficulty, and categorizes them into original, hybrid, and adaptable types. The research highlights retriever-reader and retriever-only approaches, focusing on information retrieval techniques and transformer-based models. Key findings include the prevalence of Wikipedia as a knowledge source, the importance of long-form and ambiguous questions, and the growing use of multi-hop and conversational QA. The study identifies research gaps, such as the lack of comprehensive multimodal datasets and advanced evaluation methods, and suggests future directions for Generative QA systems and artificial general intelligence.

Key findings

3

Paper digest

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

The paper aims to address the challenge of evaluating Open Domain Question Answering (ODQA) systems in the context of large language models by providing a comprehensive taxonomy of datasets and metrics . This paper focuses on categorizing datasets based on various factors such as question types, knowledge sources, and evaluation metrics to facilitate a more robust evaluation of ODQA systems . The problem addressed in the paper is not entirely new, as it builds upon existing datasets and evaluation methods but contributes by organizing and expanding the taxonomy to enhance the evaluation process for ODQA systems .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to the taxonomy of datasets and metrics for Open Domain Question Answering (ODQA) in the era of large language models . The study focuses on categorizing datasets based on the modality of input data and knowledge sources used by the system, dividing datasets into textual and multimodal categories . The goal is to refine the analysis by subcategorizing textual datasets based on the types of questions they aim to answer and exploring challenges inherent in these datasets . The paper also delves into multimodal datasets that leverage diverse modalities to enhance comprehension and answer generation capabilities, aiming to mimic how humans process information naturally .


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

The paper proposes several new ideas, methods, and models in the realm of Open Domain Question Answering (ODQA) based on a comprehensive review of datasets and evaluation metrics . Here are some key points from the paper:

  1. Semantic Evaluation: The paper emphasizes the importance of semantic evaluation in capturing the meaning of answers by focusing on semantic similarity rather than word overlap. It discusses the use of metrics like Word Mover's Distance (WMD) and Sentence Mover's Similarity (SMS) to assess semantic similarity between answers .

  2. Evaluation Metrics for Generative QA Systems: The study highlights the limitations of traditional lexical metrics in evaluating Large Language Model (LLM)-powered responses. It suggests the need for more effective automatic metrics that can reflect human judgment and detect hallucinatory outputs in LLM responses .

  3. Multimodal ODQA Datasets: The paper delves into multimodal ODQA datasets that combine text, images, and tables to enhance comprehension and answer generation capabilities. Examples include datasets like OK-VQA, S3VQA, and MIMOQA, each utilizing different techniques for visual question answering .

  4. Future Directions: The paper anticipates a continued focus on research related to multimodal datasets and advanced evaluation metrics in the ODQA domain. These advancements are seen as crucial for the development of systems that can effectively process and answer complex questions across diverse modalities, moving towards artificial general intelligence (AGI) .

In summary, the paper advocates for the development of more robust evaluation metrics, the exploration of semantic evaluation techniques, and the creation of multimodal datasets to advance the field of Open Domain Question Answering towards more comprehensive and human-like question-answering systems. The paper discusses various characteristics and advantages of new methods and models in Open Domain Question Answering (ODQA) compared to previous approaches, focusing on datasets and evaluation metrics .

  1. Multimodal ODQA Datasets:

    • The new methods emphasize the utilization of multiple modalities, such as text, images, and tables, to enhance comprehension and answer generation capabilities in ODQA systems .
    • These multimodal datasets allow systems to process information more comprehensively, mirroring how humans naturally understand and answer questions across different modalities .
    • Examples include datasets like OK-VQA, S3VQA, MIMOQA, ManyModalQA, MultiModalQA, and MMConvQA, each incorporating text, images, and tables to enable joint reasoning and answer generation .
  2. Evaluation Metrics:

    • The paper introduces new evaluation metrics, such as Semantic Evaluation using Word Mover's Distance (WMD) and Sentence Mover's Similarity (SMS) to assess semantic similarity in answers, moving beyond traditional lexical metrics .
    • For Generative QA Systems, the study highlights the need for more effective automatic metrics to evaluate Large Language Model (LLM)-powered responses, focusing on semantic similarity and human judgment to detect hallucinatory outputs .
    • The use of metrics like EM, F1, ROUGE, BLEU, and Precision@k for text and images provides a more comprehensive evaluation of answer quality and fluency in multimodal ODQA systems .
  3. Knowledge Sources:

    • The datasets leverage diverse knowledge sources such as Wikipedia, Bing image search, Reddit, and pre-trained LLMs to enhance the depth and accuracy of answers in ODQA systems .
    • By incorporating external knowledge sources and rationales, the new models enable systems to access unbounded knowledge and reasoning to provide more accurate and informative answers .

In summary, the new methods in ODQA focus on leveraging multimodal datasets, advanced evaluation metrics, and diverse knowledge sources to enhance the comprehensiveness, accuracy, and semantic understanding of question-answering systems compared to previous approaches.


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?

In the field of Open Domain Question Answering (ODQA), several notable researchers have contributed to related research. Some noteworthy researchers in this field include DPR, T5, GPT-3, Fusion-in-Decoder, DenSPI, ORQA, R3, Multi-passage BERT, REALM, and RAG . These researchers have been instrumental in developing and benchmarking ODQA models using curated datasets specifically designed for ODQA tasks.

The key to the solution mentioned in the paper revolves around the comprehensive taxonomy of datasets and metrics proposed for ODQA in the era of large language models. The taxonomy categorizes datasets based on the modality of input data and knowledge sources leveraged by the system. It further refines the analysis by subcategorizing textual datasets based on question types and multimodal datasets based on specific modalities integrated within a system . This structured approach facilitates a deeper understanding of the challenges inherent in ODQA tasks and provides a framework for evaluating system performance effectively.


How were the experiments in the paper designed?

The experiments in the paper were designed to examine three main approaches in Open Domain Question Answering (ODQA) systems :

  1. Retriever-reader approach: This approach combines Information Retrieval (IR) and Machine Reading Comprehension (MRC) techniques. The retriever component gathers relevant information from external knowledge sources, which the reader module uses to comprehend and formulate answers. Notable techniques include TF-IDF, BM25, and transformer-based models like BERT, RoBERTa, T5, BART, and GPT-3. Within this approach, readers can be extractive or generative.
  2. Retriever-only approach: This method involves using a single retriever to handle ODQA tasks, eliminating the need for a separate reader component.
  3. Multimodal ODQA: This approach involves answering questions that involve multiple modalities such as text, image, and table. Examples include datasets like OK-VQA, S3VQA, MIMOQA, and A-OKVQA .

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

The dataset used for quantitative evaluation in the context of Open Domain Question Answering (ODQA) is the SQuAD dataset . This dataset, known as the "Stanford Question Answering Dataset," consists of questions posed by crowd workers on a set of Wikipedia articles and is commonly used for evaluating ODQA systems using metrics like Exact Match (EM) and F1 scores .

Regarding the open-source availability of the code used for evaluation, the SQuAD dataset itself is openly available for research purposes, but the specific code implementations for evaluation metrics like EM and F1 scores may vary depending on the research or system being used . Researchers and developers often provide their code implementations for evaluation metrics as part of their publications or research repositories, but it is advisable to check the individual sources for the availability of open-source code related to the evaluation of ODQA systems.


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 offer substantial support for the scientific hypotheses that require verification. The study delves into the evaluation of Open Domain Question Answering (ODQA) systems, emphasizing the importance of both Human Evaluation and Automatic Evaluation methods . Human evaluation is highlighted as the gold standard for assessing system quality in ODQA, showcasing the ability of human evaluators to gauge a system's proficiency in understanding nuanced language and providing contextually appropriate responses . On the other hand, Automatic Evaluation methods, such as Lexical Matching, Semantic Similarity, and LLM-based techniques, offer valuable insights into system performance by utilizing computer programs to assess system capabilities .

The paper discusses various datasets and metrics used for evaluating ODQA systems, including multimodal datasets that combine text and images like OK-VQA, S3VQA, and MIMOQA . These datasets provide a comprehensive understanding of how ODQA systems handle diverse modalities to enhance comprehension and answer generation capabilities. Additionally, the study explores datasets like FreshQA, which focus on time-sensitive questions requiring up-to-date knowledge, and datasets like IfQA, which involve counterfactual questions with causal events .

Furthermore, the paper introduces datasets like CREPE and TruthfulQA, which challenge ODQA systems with false presuppositions and adversarial questions to test the models' truthfulness and ability to avoid generating false answers . These datasets contribute to evaluating the robustness and accuracy of ODQA systems in handling challenging scenarios. Additionally, the study highlights the importance of using pre-trained Large Language Models (LLMs) for generating answers and evaluating system performance .

In conclusion, the experiments and results presented in the paper offer a comprehensive analysis of various evaluation techniques, datasets, and metrics for assessing ODQA systems. The study provides valuable insights into the strengths and limitations of different evaluation methods, emphasizing the need for a multifaceted assessment approach that incorporates multiple metrics to thoroughly evaluate the efficacy of ODQA models .


What are the contributions of this paper?

The paper provides a comprehensive taxonomy of datasets and metrics for Open Domain Question Answering (ODQA) in the era of large language models. It categorizes datasets based on different types of questions and evaluation metrics used for ODQA tasks . The contributions of the paper include:

  • Classification of datasets such as CREPE, TruthfulQA, FreshQA, Paraphrased-SQuAD, A-OKVQA, WebQA, and more, based on the nature of questions, knowledge sources, and evaluation metrics employed .
  • Analysis of time-sensitive datasets like SituatedQA, TimeQA, and FreshQA, which focus on questions requiring fast-changing world knowledge and the use of human evaluation methods .
  • Exploration of multimodal ODQA datasets that combine text, images, and tables to enhance question comprehension and answer generation capabilities .
  • Examination of datasets with counterfactual questions, like CREPE, TruthfulQA, and IfQA, which involve questions with false presuppositions or misconceptions .
  • Evaluation of short-form datasets released between 2013 and 2019, showcasing the prevalence of Wikipedia as a primary knowledge source and the use of metrics like Exact Match (EM) and F1 scores for evaluation .
  • Benchmarking ODQA models using curated datasets like DPR, T5, GPT-3, and others to assess their performance in question answering tasks .

What work can be continued in depth?

Further research in the field of Open Domain Question Answering (ODQA) can be expanded in several areas based on the existing work:

  • Exploration of different approaches: Research can delve deeper into the retriever-reader approach, which combines Information Retrieval (IR) and Machine Reading Comprehension (MRC) techniques to enhance question-answering systems .
  • Advancements in transformer-based models: Continued investigation into transformer-based models like BERT, RoBERTa, T5, BART, and GPT-3 can lead to improved performance in ODQA systems .
  • Enhancement of evaluation metrics: Further development and refinement of standardized evaluation metrics can facilitate better comparisons between different ODQA systems, enabling researchers to objectively measure progress in the field .
  • Exploration of multimodal modalities: Research can focus on incorporating multimodal datasets and techniques to enhance the performance of ODQA systems across different types of data sources .
  • Addressing current challenges: Identifying and tackling the existing challenges in ODQA systems can pave the way for more robust and effective question-answering models .
  • Future research directions: Identifying promising avenues for future research and development in ODQA can lead to innovative solutions and advancements in the field .

Tables

8

Introduction
Background
Evolution of ODQA systems
Importance of diverse datasets and evaluation methods
Objective
To provide a structured overview of ODQA datasets and metrics
To analyze retrieval and reader models
To identify research gaps and future directions
Taxonomy of ODQA Datasets
Modality Classification
Textual Datasets
Single-sentence QA
Long-form QA
Ambiguous questions
Multimodal Datasets
Visual QA
Conversational QA
Cross-modal understanding
Dataset Types
Original Datasets
Created from scratch
Hybrid Datasets
Merging multiple sources
Adaptable Datasets
Suitable for customization
Approaches and Techniques
Retriever-Reader Models
Information Retrieval Techniques
Query-based retrieval
Passage ranking
Transformer-Based Models
BERT, RoBERTa, etc.
Fine-tuning and pre-training
Key Findings
Knowledge Sources
Prevalence of Wikipedia
Question Characteristics
Long-form and ambiguous questions
Multi-hop reasoning
Challenges and Trends
Multi-modal understanding
Conversational QA systems
Evaluation Metrics
20 Evaluated Metrics
Exact match
F1 score
ROUGE
BLEU
Human evaluation
Limitations and Gaps
Comprehensive multimodal evaluation
Advanced metrics for generative QA
Future Directions
Generative QA systems
Artificial General Intelligence (AGI) research
Multimodal dataset creation
Enhanced evaluation methodologies
Conclusion
Summary of key insights and implications for the field
Call to action for future research and collaboration.
Basic info
papers
computation and language
information retrieval
machine learning
artificial intelligence
Advanced features
Insights
Which modality is primarily used in the open domain question answering datasets discussed?
What are the two main approaches to question answering mentioned, and which techniques do they emphasize?
What are the three categories of datasets identified in the study, and what distinguishes them?
What types of question answering datasets does the study classify, and how many are there in total?

Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models

Akchay Srivastava, Atif Memon·June 19, 2024

Summary

This study presents a comprehensive taxonomy of 52 open domain question answering (ODQA) datasets and 20 evaluation metrics, covering textual and multimodal modalities. It differentiates datasets based on modality, question difficulty, and categorizes them into original, hybrid, and adaptable types. The research highlights retriever-reader and retriever-only approaches, focusing on information retrieval techniques and transformer-based models. Key findings include the prevalence of Wikipedia as a knowledge source, the importance of long-form and ambiguous questions, and the growing use of multi-hop and conversational QA. The study identifies research gaps, such as the lack of comprehensive multimodal datasets and advanced evaluation methods, and suggests future directions for Generative QA systems and artificial general intelligence.
Mind map
Fine-tuning and pre-training
BERT, RoBERTa, etc.
Passage ranking
Query-based retrieval
Cross-modal understanding
Conversational QA
Visual QA
Ambiguous questions
Long-form QA
Single-sentence QA
Advanced metrics for generative QA
Comprehensive multimodal evaluation
Human evaluation
BLEU
ROUGE
F1 score
Exact match
Conversational QA systems
Multi-modal understanding
Multi-hop reasoning
Long-form and ambiguous questions
Prevalence of Wikipedia
Transformer-Based Models
Information Retrieval Techniques
Suitable for customization
Adaptable Datasets
Merging multiple sources
Hybrid Datasets
Created from scratch
Original Datasets
Multimodal Datasets
Textual Datasets
To identify research gaps and future directions
To analyze retrieval and reader models
To provide a structured overview of ODQA datasets and metrics
Importance of diverse datasets and evaluation methods
Evolution of ODQA systems
Call to action for future research and collaboration.
Summary of key insights and implications for the field
Enhanced evaluation methodologies
Multimodal dataset creation
Artificial General Intelligence (AGI) research
Generative QA systems
Limitations and Gaps
20 Evaluated Metrics
Challenges and Trends
Question Characteristics
Knowledge Sources
Retriever-Reader Models
Dataset Types
Modality Classification
Objective
Background
Conclusion
Future Directions
Evaluation Metrics
Key Findings
Approaches and Techniques
Taxonomy of ODQA Datasets
Introduction
Outline
Introduction
Background
Evolution of ODQA systems
Importance of diverse datasets and evaluation methods
Objective
To provide a structured overview of ODQA datasets and metrics
To analyze retrieval and reader models
To identify research gaps and future directions
Taxonomy of ODQA Datasets
Modality Classification
Textual Datasets
Single-sentence QA
Long-form QA
Ambiguous questions
Multimodal Datasets
Visual QA
Conversational QA
Cross-modal understanding
Dataset Types
Original Datasets
Created from scratch
Hybrid Datasets
Merging multiple sources
Adaptable Datasets
Suitable for customization
Approaches and Techniques
Retriever-Reader Models
Information Retrieval Techniques
Query-based retrieval
Passage ranking
Transformer-Based Models
BERT, RoBERTa, etc.
Fine-tuning and pre-training
Key Findings
Knowledge Sources
Prevalence of Wikipedia
Question Characteristics
Long-form and ambiguous questions
Multi-hop reasoning
Challenges and Trends
Multi-modal understanding
Conversational QA systems
Evaluation Metrics
20 Evaluated Metrics
Exact match
F1 score
ROUGE
BLEU
Human evaluation
Limitations and Gaps
Comprehensive multimodal evaluation
Advanced metrics for generative QA
Future Directions
Generative QA systems
Artificial General Intelligence (AGI) research
Multimodal dataset creation
Enhanced evaluation methodologies
Conclusion
Summary of key insights and implications for the field
Call to action for future research and collaboration.
Key findings
3

Paper digest

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

The paper aims to address the challenge of evaluating Open Domain Question Answering (ODQA) systems in the context of large language models by providing a comprehensive taxonomy of datasets and metrics . This paper focuses on categorizing datasets based on various factors such as question types, knowledge sources, and evaluation metrics to facilitate a more robust evaluation of ODQA systems . The problem addressed in the paper is not entirely new, as it builds upon existing datasets and evaluation methods but contributes by organizing and expanding the taxonomy to enhance the evaluation process for ODQA systems .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to the taxonomy of datasets and metrics for Open Domain Question Answering (ODQA) in the era of large language models . The study focuses on categorizing datasets based on the modality of input data and knowledge sources used by the system, dividing datasets into textual and multimodal categories . The goal is to refine the analysis by subcategorizing textual datasets based on the types of questions they aim to answer and exploring challenges inherent in these datasets . The paper also delves into multimodal datasets that leverage diverse modalities to enhance comprehension and answer generation capabilities, aiming to mimic how humans process information naturally .


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

The paper proposes several new ideas, methods, and models in the realm of Open Domain Question Answering (ODQA) based on a comprehensive review of datasets and evaluation metrics . Here are some key points from the paper:

  1. Semantic Evaluation: The paper emphasizes the importance of semantic evaluation in capturing the meaning of answers by focusing on semantic similarity rather than word overlap. It discusses the use of metrics like Word Mover's Distance (WMD) and Sentence Mover's Similarity (SMS) to assess semantic similarity between answers .

  2. Evaluation Metrics for Generative QA Systems: The study highlights the limitations of traditional lexical metrics in evaluating Large Language Model (LLM)-powered responses. It suggests the need for more effective automatic metrics that can reflect human judgment and detect hallucinatory outputs in LLM responses .

  3. Multimodal ODQA Datasets: The paper delves into multimodal ODQA datasets that combine text, images, and tables to enhance comprehension and answer generation capabilities. Examples include datasets like OK-VQA, S3VQA, and MIMOQA, each utilizing different techniques for visual question answering .

  4. Future Directions: The paper anticipates a continued focus on research related to multimodal datasets and advanced evaluation metrics in the ODQA domain. These advancements are seen as crucial for the development of systems that can effectively process and answer complex questions across diverse modalities, moving towards artificial general intelligence (AGI) .

In summary, the paper advocates for the development of more robust evaluation metrics, the exploration of semantic evaluation techniques, and the creation of multimodal datasets to advance the field of Open Domain Question Answering towards more comprehensive and human-like question-answering systems. The paper discusses various characteristics and advantages of new methods and models in Open Domain Question Answering (ODQA) compared to previous approaches, focusing on datasets and evaluation metrics .

  1. Multimodal ODQA Datasets:

    • The new methods emphasize the utilization of multiple modalities, such as text, images, and tables, to enhance comprehension and answer generation capabilities in ODQA systems .
    • These multimodal datasets allow systems to process information more comprehensively, mirroring how humans naturally understand and answer questions across different modalities .
    • Examples include datasets like OK-VQA, S3VQA, MIMOQA, ManyModalQA, MultiModalQA, and MMConvQA, each incorporating text, images, and tables to enable joint reasoning and answer generation .
  2. Evaluation Metrics:

    • The paper introduces new evaluation metrics, such as Semantic Evaluation using Word Mover's Distance (WMD) and Sentence Mover's Similarity (SMS) to assess semantic similarity in answers, moving beyond traditional lexical metrics .
    • For Generative QA Systems, the study highlights the need for more effective automatic metrics to evaluate Large Language Model (LLM)-powered responses, focusing on semantic similarity and human judgment to detect hallucinatory outputs .
    • The use of metrics like EM, F1, ROUGE, BLEU, and Precision@k for text and images provides a more comprehensive evaluation of answer quality and fluency in multimodal ODQA systems .
  3. Knowledge Sources:

    • The datasets leverage diverse knowledge sources such as Wikipedia, Bing image search, Reddit, and pre-trained LLMs to enhance the depth and accuracy of answers in ODQA systems .
    • By incorporating external knowledge sources and rationales, the new models enable systems to access unbounded knowledge and reasoning to provide more accurate and informative answers .

In summary, the new methods in ODQA focus on leveraging multimodal datasets, advanced evaluation metrics, and diverse knowledge sources to enhance the comprehensiveness, accuracy, and semantic understanding of question-answering systems compared to previous approaches.


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?

In the field of Open Domain Question Answering (ODQA), several notable researchers have contributed to related research. Some noteworthy researchers in this field include DPR, T5, GPT-3, Fusion-in-Decoder, DenSPI, ORQA, R3, Multi-passage BERT, REALM, and RAG . These researchers have been instrumental in developing and benchmarking ODQA models using curated datasets specifically designed for ODQA tasks.

The key to the solution mentioned in the paper revolves around the comprehensive taxonomy of datasets and metrics proposed for ODQA in the era of large language models. The taxonomy categorizes datasets based on the modality of input data and knowledge sources leveraged by the system. It further refines the analysis by subcategorizing textual datasets based on question types and multimodal datasets based on specific modalities integrated within a system . This structured approach facilitates a deeper understanding of the challenges inherent in ODQA tasks and provides a framework for evaluating system performance effectively.


How were the experiments in the paper designed?

The experiments in the paper were designed to examine three main approaches in Open Domain Question Answering (ODQA) systems :

  1. Retriever-reader approach: This approach combines Information Retrieval (IR) and Machine Reading Comprehension (MRC) techniques. The retriever component gathers relevant information from external knowledge sources, which the reader module uses to comprehend and formulate answers. Notable techniques include TF-IDF, BM25, and transformer-based models like BERT, RoBERTa, T5, BART, and GPT-3. Within this approach, readers can be extractive or generative.
  2. Retriever-only approach: This method involves using a single retriever to handle ODQA tasks, eliminating the need for a separate reader component.
  3. Multimodal ODQA: This approach involves answering questions that involve multiple modalities such as text, image, and table. Examples include datasets like OK-VQA, S3VQA, MIMOQA, and A-OKVQA .

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

The dataset used for quantitative evaluation in the context of Open Domain Question Answering (ODQA) is the SQuAD dataset . This dataset, known as the "Stanford Question Answering Dataset," consists of questions posed by crowd workers on a set of Wikipedia articles and is commonly used for evaluating ODQA systems using metrics like Exact Match (EM) and F1 scores .

Regarding the open-source availability of the code used for evaluation, the SQuAD dataset itself is openly available for research purposes, but the specific code implementations for evaluation metrics like EM and F1 scores may vary depending on the research or system being used . Researchers and developers often provide their code implementations for evaluation metrics as part of their publications or research repositories, but it is advisable to check the individual sources for the availability of open-source code related to the evaluation of ODQA systems.


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 offer substantial support for the scientific hypotheses that require verification. The study delves into the evaluation of Open Domain Question Answering (ODQA) systems, emphasizing the importance of both Human Evaluation and Automatic Evaluation methods . Human evaluation is highlighted as the gold standard for assessing system quality in ODQA, showcasing the ability of human evaluators to gauge a system's proficiency in understanding nuanced language and providing contextually appropriate responses . On the other hand, Automatic Evaluation methods, such as Lexical Matching, Semantic Similarity, and LLM-based techniques, offer valuable insights into system performance by utilizing computer programs to assess system capabilities .

The paper discusses various datasets and metrics used for evaluating ODQA systems, including multimodal datasets that combine text and images like OK-VQA, S3VQA, and MIMOQA . These datasets provide a comprehensive understanding of how ODQA systems handle diverse modalities to enhance comprehension and answer generation capabilities. Additionally, the study explores datasets like FreshQA, which focus on time-sensitive questions requiring up-to-date knowledge, and datasets like IfQA, which involve counterfactual questions with causal events .

Furthermore, the paper introduces datasets like CREPE and TruthfulQA, which challenge ODQA systems with false presuppositions and adversarial questions to test the models' truthfulness and ability to avoid generating false answers . These datasets contribute to evaluating the robustness and accuracy of ODQA systems in handling challenging scenarios. Additionally, the study highlights the importance of using pre-trained Large Language Models (LLMs) for generating answers and evaluating system performance .

In conclusion, the experiments and results presented in the paper offer a comprehensive analysis of various evaluation techniques, datasets, and metrics for assessing ODQA systems. The study provides valuable insights into the strengths and limitations of different evaluation methods, emphasizing the need for a multifaceted assessment approach that incorporates multiple metrics to thoroughly evaluate the efficacy of ODQA models .


What are the contributions of this paper?

The paper provides a comprehensive taxonomy of datasets and metrics for Open Domain Question Answering (ODQA) in the era of large language models. It categorizes datasets based on different types of questions and evaluation metrics used for ODQA tasks . The contributions of the paper include:

  • Classification of datasets such as CREPE, TruthfulQA, FreshQA, Paraphrased-SQuAD, A-OKVQA, WebQA, and more, based on the nature of questions, knowledge sources, and evaluation metrics employed .
  • Analysis of time-sensitive datasets like SituatedQA, TimeQA, and FreshQA, which focus on questions requiring fast-changing world knowledge and the use of human evaluation methods .
  • Exploration of multimodal ODQA datasets that combine text, images, and tables to enhance question comprehension and answer generation capabilities .
  • Examination of datasets with counterfactual questions, like CREPE, TruthfulQA, and IfQA, which involve questions with false presuppositions or misconceptions .
  • Evaluation of short-form datasets released between 2013 and 2019, showcasing the prevalence of Wikipedia as a primary knowledge source and the use of metrics like Exact Match (EM) and F1 scores for evaluation .
  • Benchmarking ODQA models using curated datasets like DPR, T5, GPT-3, and others to assess their performance in question answering tasks .

What work can be continued in depth?

Further research in the field of Open Domain Question Answering (ODQA) can be expanded in several areas based on the existing work:

  • Exploration of different approaches: Research can delve deeper into the retriever-reader approach, which combines Information Retrieval (IR) and Machine Reading Comprehension (MRC) techniques to enhance question-answering systems .
  • Advancements in transformer-based models: Continued investigation into transformer-based models like BERT, RoBERTa, T5, BART, and GPT-3 can lead to improved performance in ODQA systems .
  • Enhancement of evaluation metrics: Further development and refinement of standardized evaluation metrics can facilitate better comparisons between different ODQA systems, enabling researchers to objectively measure progress in the field .
  • Exploration of multimodal modalities: Research can focus on incorporating multimodal datasets and techniques to enhance the performance of ODQA systems across different types of data sources .
  • Addressing current challenges: Identifying and tackling the existing challenges in ODQA systems can pave the way for more robust and effective question-answering models .
  • Future research directions: Identifying promising avenues for future research and development in ODQA can lead to innovative solutions and advancements in the field .
Tables
8
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