RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content

Joao Monteiro, Pierre-Andre Noel, Etienne Marcotte, Sai Rajeswar, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian·June 17, 2024

Summary

The paper introduces REPLIQA, a question-answering dataset for benchmarking large language models on unseen reference content, addressing data contamination issues in existing datasets. It consists of five splits with human-generated documents, questions, and answers, evaluating models' ability to find relevant information within the provided documents. The dataset aims to test context-dependent language understanding and is used to compare various LLMs, revealing a need for unseen data evaluation. REPLIQA addresses the reliance on pre-training knowledge and the importance of datasets like this for accurate performance assessments. The study also highlights the use of retrieval-augmented generation and the limitations of model scaling in terms of reading comprehension. The project is ongoing, with a focus on transparency and continuous improvement.

Key findings

10

Paper digest

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

The paper aims to address the issue of data contamination in evaluating language models, particularly in the context of question-answering tasks . This problem is not entirely new, as previous research has highlighted challenges related to data contamination and evaluation practices in closed-source large language models (LLMs) . The paper emphasizes the importance of ensuring that language models are evaluated on samples that were previously inaccessible on the web to prevent contamination and maintain the integrity of the evaluation process .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the creation of a novel test benchmark, REPLIQA, for evaluating language models using samples that were previously inaccessible on the web. The goal is to assess open-domain question answering based on reference documents and document topic retrieval . The paper introduces REPLIQA, which consists of a total of 89,770 question-answer pairs based on 17,954 reference documents. Human content writers were hired to invent reference documents covering a range of topics about imaginary scenarios, people, and places, along with question-answer pairs that are not answerable without the associated reference document .


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

The paper introduces several innovative ideas, methods, and models in the field of language processing and benchmarking LLMs on unseen reference content . Here are some key points from the paper:

  1. Data Contamination and Evaluation Malpractices: The paper by Simone Balloccu et al. discusses data contamination and evaluation malpractices in closed-source LLMs . This highlights the importance of ensuring data integrity and proper evaluation methods in language model development.

  2. Fast-DetectGPT: Guangsheng Bao et al. present Fast-DetectGPT, an efficient zero-shot detection method for machine-generated text using conditional probability curvature . This method aims to enhance the detection capabilities of machine-generated text.

  3. Foundation Model Transparency Index: Rishi Bommasani et al. introduce the Foundation Model Transparency Index v1.1, which focuses on transparency aspects related to foundation models . This index provides insights into the transparency of these models.

  4. Gemini Models: The Gemini Team presents Gemini, a family of highly capable multimodal models . These models likely integrate multiple modalities for improved performance in various tasks.

  5. HotpotQA Dataset: Zhilin Yang et al. introduce the HotpotQA dataset, designed for diverse and explainable multi-hop question answering tasks . This dataset contributes to advancing question-answering research with a focus on multi-hop reasoning.

  6. Retrieval-Augmented Generation (RAG): Recent advancements have integrated Retrieval-Augmented Generation (RAG) to enhance the efficiency of In-Context Learning (ICL) in language models . RAG enables models to access and utilize relevant external knowledge dynamically, improving performance across various tasks.

  7. Benchmarking Benchmark Leakage: Ruijie Xu et al. discuss benchmarking benchmark leakage in large language models . This research sheds light on potential issues related to benchmark data leakage in the context of large language models.

These contributions reflect the diverse range of topics covered in the paper, from data integrity and evaluation practices to the development of new models and datasets for advancing language processing research. The paper "RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content" introduces several characteristics and advantages compared to previous methods in the field of language processing and benchmarking LLMs on unseen reference content :

  1. Data Contamination and Evaluation Malpractices: The paper addresses the issue of data contamination and evaluation malpractices in closed-source LLMs, emphasizing the importance of ensuring data integrity and proper evaluation methods .

  2. Fast-DetectGPT: The introduction of Fast-DetectGPT by Guangsheng Bao et al. presents an efficient zero-shot detection method for machine-generated text using conditional probability curvature, enhancing the detection capabilities of machine-generated text .

  3. Foundation Model Transparency Index: The paper by Rishi Bommasani et al. introduces the Foundation Model Transparency Index v1.1, focusing on transparency aspects related to foundation models, providing insights into the transparency of these models .

  4. Gemini Models: The Gemini Team presents Gemini, a family of highly capable multimodal models, likely integrating multiple modalities for improved performance in various tasks .

  5. HotpotQA Dataset: Zhilin Yang et al. introduce the HotpotQA dataset, designed for diverse and explainable multi-hop question answering tasks, contributing to advancing question-answering research with a focus on multi-hop reasoning .

  6. Retrieval-Augmented Generation (RAG): Recent advancements integrating Retrieval-Augmented Generation (RAG) have been made to enhance the efficiency of In-Context Learning (ICL) in language models, enabling models to access and utilize relevant external knowledge dynamically, improving performance across various tasks .

  7. Benchmarking Benchmark Leakage: The paper discusses benchmarking benchmark leakage in large language models, highlighting concerns about data leakage and the need for datasets like RepLiQA, designed to be unseen by LLMs, to provide a true test of their learning and generalization capabilities .

These advancements and methodologies contribute to the field by addressing key challenges, enhancing model capabilities, and providing more transparent and reliable evaluation methods for language 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 works exist in the field, and notable researchers have contributed to this topic. Some of the noteworthy researchers mentioned in the context include Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D Manning, Hugh Zhang, Jeff Da, Dean Lee, and many others . The key to the solution mentioned in the paper involves conducting comprehensive research, utilizing a variety of online resources, and carefully managing the process of deciding on names of places, people, and organizations within the texts to ensure realism and avoid legal or ethical issues .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The experiments were conducted on the zeroth split of the REPLIQA dataset, with subsequent splits planned for release every two months starting December 2024 .
  • The paper introduced REPLIQA, a dataset comprising approximately 90,000 question-answer pairs and 18,000 reference documents across 17 categories for testing Language Models (LLMs) on unseen data .
  • The experiments involved evaluating 18 state-of-the-art LLMs to assess their reliance on internal memory acquired during pre-training versus reference documents provided via prompting. The study also examined scaling effects and the LLMs' ability to abstain from answering .

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

The dataset used for quantitative evaluation in the context is called REPLIQA (Repository of Likely Question-Answer data) . The code for REPLIQA is not explicitly mentioned as open source in the provided context.


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 require verification. The paper introduces REPLIQA, a novel dataset designed to evaluate language models (LLMs) using previously inaccessible samples . The dataset consists of question-answer pairs based on reference documents and document topic retrieval, aiming to assess open-domain question answering . The study demonstrates the importance of complementary evaluations to ensure a model's performance extends to new reference documents beyond those it may have been pre-trained on, like Wikipedia content . This approach addresses the challenge of attributing good performance to acquired reading skills rather than memorization .

Moreover, the paper evaluates models on tasks such as testing abstention ability and topic retrieval, showcasing the models' capabilities in admitting when an answer is not found and determining document topics in a zero-shot fashion . The results highlight the differences in model performance for global understanding of documents versus finding specific information within a large document . For instance, the evaluation reveals that different model abilities are required for these distinct tasks, emphasizing the need for comprehensive assessments .

Furthermore, the study employs Fast-DetectGPT to test for LLM-generated content, indicating the likelihood of samples being from an LLM based on detection scores . This analysis provides insights into the involvement of LLMs in creating the dataset and the extent to which they may have influenced the questions and answers . By assessing changes in detection scores after adding questions and answers to contexts, the study sheds light on the potential impact of LLMs on question-answer generation .

Overall, the experiments and results in the paper offer robust support for the scientific hypotheses under investigation by introducing a new dataset, conducting diverse evaluations, and analyzing the influence of LLMs on question answering and document understanding tasks.


What are the contributions of this paper?

The paper "RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content" makes several key contributions:

  • It introduces a new test dataset named REPLIQA, specifically designed for question-answering and topic retrieval tasks, consisting of five splits of test sets, with four splits not previously released to the internet or exposed to LLM APIs .
  • The dataset includes samples comprising a reference document crafted by a human annotator depicting an imaginary scenario, a question about the document's topic, and a ground-truth answer derived directly from the information in the document .
  • The paper aims to address the challenge of evaluating language models on test splits that may have leaked into the training set, which can lead to misleading conclusions. By providing a new test dataset like REPLIQA, it fosters sound evaluation of language models .
  • It contributes to the field of natural language processing by providing a benchmark dataset that helps in assessing the ability of language models to generalize to novel samples, ensuring the validity of conclusions drawn from model evaluations .

What work can be continued in depth?

To delve deeper into the work outlined in the provided context, further exploration can focus on the following aspects:

  • Evaluation of Language Models: The assessment of popular state-of-the-art Large Language Models (LLMs) to determine their ability to comprehend provided contexts and accurately respond to user queries, known as question answering. This evaluation involves testing the models on metrics such as F1 score and recall, as well as their capability to detect and handle unanswerable questions .
  • Benchmarking LLMs: The benchmarking of various widely-used LLMs, including GPT-3.5, GPT-4O, LLAMA 3, GEMINI 1.0 and 1.5, WIZARDLM, MISTRAL, MIXTRAL, COMMAND R, COMMAND R+, ARCTIC, and others, to assess their performance in reading comprehension and topic retrieval tasks. This benchmarking process utilizes a unified framework called OPENROUTER for inference .
  • Dataset Creation and Maintenance: The ongoing maintenance and enhancement of the REPLIQA dataset for benchmarking LLMs on unseen reference content. This involves considering incoming recommendations, updating the dataset accordingly, and ensuring its active maintenance until at least the end of 2025. The dataset comprises a substantial number of question-answer pairs based on diverse reference documents .
  • Content Creation Guidelines: The adherence to specific guidelines for creating high-quality content, including conducting thorough research, ensuring factual accuracy, incorporating originality and creativity, using diverse and inclusive content, following legal and ethical standards, and engaging in document review and revision processes. These guidelines aim to produce informative, engaging, and accurate content for evaluation purposes .

Tables

1

Introduction
Background
Data contamination issues in existing QA datasets
Importance of evaluating context-dependent language understanding
Objective
To benchmark LLMs on unseen reference content
Address pre-training knowledge reliance and dataset impact on performance
Encourage transparency and continuous improvement
Dataset Construction
Document Generation
Human-generated documents for diverse content
Question-Answer Pairs
Creation of questions and answers targeting relevant information
Contextual relevance emphasized
Dataset Structure
Five Splits
Description of each split (train, validation, test, development, and unseen)
Purpose of each for model evaluation
Evaluation Metrics
Assessing models' ability to find relevant information
Focus on context-dependent comprehension
Methodology
Data Collection
Selection criteria for human-generated content
Ensuring diversity and authenticity
Data Preprocessing
Cleaning and formatting for model input
Handling of unseen reference content
Model Analysis
Retrieval-Augmented Generation
The role of retrieval in enhancing generation performance
Limitations of current LLMs in reading comprehension
Scaling Insights
Impact of model size on comprehension tasks
The need for unseen data to measure true capabilities
Transparency and Ongoing Development
Project goals for continuous improvement
Plans for addressing identified limitations
Conclusion
Significance of REPLIQA for benchmarking and future research
Call to action for more datasets addressing unseen content challenges
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
How does the study using REPLIQA contribute to our understanding of large language models' context-dependent language understanding and reliance on pre-training knowledge?
What are the key factors evaluated in the five splits of REPLIQA, and how do they test models' abilities?
What is REPLIQA, and what purpose does it serve in the field of language model evaluation?
How does REPLIQA address data contamination issues in existing question-answering datasets?

RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content

Joao Monteiro, Pierre-Andre Noel, Etienne Marcotte, Sai Rajeswar, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian·June 17, 2024

Summary

The paper introduces REPLIQA, a question-answering dataset for benchmarking large language models on unseen reference content, addressing data contamination issues in existing datasets. It consists of five splits with human-generated documents, questions, and answers, evaluating models' ability to find relevant information within the provided documents. The dataset aims to test context-dependent language understanding and is used to compare various LLMs, revealing a need for unseen data evaluation. REPLIQA addresses the reliance on pre-training knowledge and the importance of datasets like this for accurate performance assessments. The study also highlights the use of retrieval-augmented generation and the limitations of model scaling in terms of reading comprehension. The project is ongoing, with a focus on transparency and continuous improvement.
Mind map
The need for unseen data to measure true capabilities
Impact of model size on comprehension tasks
Limitations of current LLMs in reading comprehension
The role of retrieval in enhancing generation performance
Handling of unseen reference content
Cleaning and formatting for model input
Ensuring diversity and authenticity
Selection criteria for human-generated content
Purpose of each for model evaluation
Description of each split (train, validation, test, development, and unseen)
Contextual relevance emphasized
Creation of questions and answers targeting relevant information
Human-generated documents for diverse content
Encourage transparency and continuous improvement
Address pre-training knowledge reliance and dataset impact on performance
To benchmark LLMs on unseen reference content
Importance of evaluating context-dependent language understanding
Data contamination issues in existing QA datasets
Call to action for more datasets addressing unseen content challenges
Significance of REPLIQA for benchmarking and future research
Plans for addressing identified limitations
Project goals for continuous improvement
Scaling Insights
Retrieval-Augmented Generation
Data Preprocessing
Data Collection
Focus on context-dependent comprehension
Assessing models' ability to find relevant information
Five Splits
Question-Answer Pairs
Document Generation
Objective
Background
Conclusion
Transparency and Ongoing Development
Model Analysis
Methodology
Evaluation Metrics
Dataset Structure
Dataset Construction
Introduction
Outline
Introduction
Background
Data contamination issues in existing QA datasets
Importance of evaluating context-dependent language understanding
Objective
To benchmark LLMs on unseen reference content
Address pre-training knowledge reliance and dataset impact on performance
Encourage transparency and continuous improvement
Dataset Construction
Document Generation
Human-generated documents for diverse content
Question-Answer Pairs
Creation of questions and answers targeting relevant information
Contextual relevance emphasized
Dataset Structure
Five Splits
Description of each split (train, validation, test, development, and unseen)
Purpose of each for model evaluation
Evaluation Metrics
Assessing models' ability to find relevant information
Focus on context-dependent comprehension
Methodology
Data Collection
Selection criteria for human-generated content
Ensuring diversity and authenticity
Data Preprocessing
Cleaning and formatting for model input
Handling of unseen reference content
Model Analysis
Retrieval-Augmented Generation
The role of retrieval in enhancing generation performance
Limitations of current LLMs in reading comprehension
Scaling Insights
Impact of model size on comprehension tasks
The need for unseen data to measure true capabilities
Transparency and Ongoing Development
Project goals for continuous improvement
Plans for addressing identified limitations
Conclusion
Significance of REPLIQA for benchmarking and future research
Call to action for more datasets addressing unseen content challenges
Key findings
10

Paper digest

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

The paper aims to address the issue of data contamination in evaluating language models, particularly in the context of question-answering tasks . This problem is not entirely new, as previous research has highlighted challenges related to data contamination and evaluation practices in closed-source large language models (LLMs) . The paper emphasizes the importance of ensuring that language models are evaluated on samples that were previously inaccessible on the web to prevent contamination and maintain the integrity of the evaluation process .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the creation of a novel test benchmark, REPLIQA, for evaluating language models using samples that were previously inaccessible on the web. The goal is to assess open-domain question answering based on reference documents and document topic retrieval . The paper introduces REPLIQA, which consists of a total of 89,770 question-answer pairs based on 17,954 reference documents. Human content writers were hired to invent reference documents covering a range of topics about imaginary scenarios, people, and places, along with question-answer pairs that are not answerable without the associated reference document .


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

The paper introduces several innovative ideas, methods, and models in the field of language processing and benchmarking LLMs on unseen reference content . Here are some key points from the paper:

  1. Data Contamination and Evaluation Malpractices: The paper by Simone Balloccu et al. discusses data contamination and evaluation malpractices in closed-source LLMs . This highlights the importance of ensuring data integrity and proper evaluation methods in language model development.

  2. Fast-DetectGPT: Guangsheng Bao et al. present Fast-DetectGPT, an efficient zero-shot detection method for machine-generated text using conditional probability curvature . This method aims to enhance the detection capabilities of machine-generated text.

  3. Foundation Model Transparency Index: Rishi Bommasani et al. introduce the Foundation Model Transparency Index v1.1, which focuses on transparency aspects related to foundation models . This index provides insights into the transparency of these models.

  4. Gemini Models: The Gemini Team presents Gemini, a family of highly capable multimodal models . These models likely integrate multiple modalities for improved performance in various tasks.

  5. HotpotQA Dataset: Zhilin Yang et al. introduce the HotpotQA dataset, designed for diverse and explainable multi-hop question answering tasks . This dataset contributes to advancing question-answering research with a focus on multi-hop reasoning.

  6. Retrieval-Augmented Generation (RAG): Recent advancements have integrated Retrieval-Augmented Generation (RAG) to enhance the efficiency of In-Context Learning (ICL) in language models . RAG enables models to access and utilize relevant external knowledge dynamically, improving performance across various tasks.

  7. Benchmarking Benchmark Leakage: Ruijie Xu et al. discuss benchmarking benchmark leakage in large language models . This research sheds light on potential issues related to benchmark data leakage in the context of large language models.

These contributions reflect the diverse range of topics covered in the paper, from data integrity and evaluation practices to the development of new models and datasets for advancing language processing research. The paper "RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content" introduces several characteristics and advantages compared to previous methods in the field of language processing and benchmarking LLMs on unseen reference content :

  1. Data Contamination and Evaluation Malpractices: The paper addresses the issue of data contamination and evaluation malpractices in closed-source LLMs, emphasizing the importance of ensuring data integrity and proper evaluation methods .

  2. Fast-DetectGPT: The introduction of Fast-DetectGPT by Guangsheng Bao et al. presents an efficient zero-shot detection method for machine-generated text using conditional probability curvature, enhancing the detection capabilities of machine-generated text .

  3. Foundation Model Transparency Index: The paper by Rishi Bommasani et al. introduces the Foundation Model Transparency Index v1.1, focusing on transparency aspects related to foundation models, providing insights into the transparency of these models .

  4. Gemini Models: The Gemini Team presents Gemini, a family of highly capable multimodal models, likely integrating multiple modalities for improved performance in various tasks .

  5. HotpotQA Dataset: Zhilin Yang et al. introduce the HotpotQA dataset, designed for diverse and explainable multi-hop question answering tasks, contributing to advancing question-answering research with a focus on multi-hop reasoning .

  6. Retrieval-Augmented Generation (RAG): Recent advancements integrating Retrieval-Augmented Generation (RAG) have been made to enhance the efficiency of In-Context Learning (ICL) in language models, enabling models to access and utilize relevant external knowledge dynamically, improving performance across various tasks .

  7. Benchmarking Benchmark Leakage: The paper discusses benchmarking benchmark leakage in large language models, highlighting concerns about data leakage and the need for datasets like RepLiQA, designed to be unseen by LLMs, to provide a true test of their learning and generalization capabilities .

These advancements and methodologies contribute to the field by addressing key challenges, enhancing model capabilities, and providing more transparent and reliable evaluation methods for language 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 works exist in the field, and notable researchers have contributed to this topic. Some of the noteworthy researchers mentioned in the context include Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D Manning, Hugh Zhang, Jeff Da, Dean Lee, and many others . The key to the solution mentioned in the paper involves conducting comprehensive research, utilizing a variety of online resources, and carefully managing the process of deciding on names of places, people, and organizations within the texts to ensure realism and avoid legal or ethical issues .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The experiments were conducted on the zeroth split of the REPLIQA dataset, with subsequent splits planned for release every two months starting December 2024 .
  • The paper introduced REPLIQA, a dataset comprising approximately 90,000 question-answer pairs and 18,000 reference documents across 17 categories for testing Language Models (LLMs) on unseen data .
  • The experiments involved evaluating 18 state-of-the-art LLMs to assess their reliance on internal memory acquired during pre-training versus reference documents provided via prompting. The study also examined scaling effects and the LLMs' ability to abstain from answering .

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

The dataset used for quantitative evaluation in the context is called REPLIQA (Repository of Likely Question-Answer data) . The code for REPLIQA is not explicitly mentioned as open source in the provided context.


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 require verification. The paper introduces REPLIQA, a novel dataset designed to evaluate language models (LLMs) using previously inaccessible samples . The dataset consists of question-answer pairs based on reference documents and document topic retrieval, aiming to assess open-domain question answering . The study demonstrates the importance of complementary evaluations to ensure a model's performance extends to new reference documents beyond those it may have been pre-trained on, like Wikipedia content . This approach addresses the challenge of attributing good performance to acquired reading skills rather than memorization .

Moreover, the paper evaluates models on tasks such as testing abstention ability and topic retrieval, showcasing the models' capabilities in admitting when an answer is not found and determining document topics in a zero-shot fashion . The results highlight the differences in model performance for global understanding of documents versus finding specific information within a large document . For instance, the evaluation reveals that different model abilities are required for these distinct tasks, emphasizing the need for comprehensive assessments .

Furthermore, the study employs Fast-DetectGPT to test for LLM-generated content, indicating the likelihood of samples being from an LLM based on detection scores . This analysis provides insights into the involvement of LLMs in creating the dataset and the extent to which they may have influenced the questions and answers . By assessing changes in detection scores after adding questions and answers to contexts, the study sheds light on the potential impact of LLMs on question-answer generation .

Overall, the experiments and results in the paper offer robust support for the scientific hypotheses under investigation by introducing a new dataset, conducting diverse evaluations, and analyzing the influence of LLMs on question answering and document understanding tasks.


What are the contributions of this paper?

The paper "RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content" makes several key contributions:

  • It introduces a new test dataset named REPLIQA, specifically designed for question-answering and topic retrieval tasks, consisting of five splits of test sets, with four splits not previously released to the internet or exposed to LLM APIs .
  • The dataset includes samples comprising a reference document crafted by a human annotator depicting an imaginary scenario, a question about the document's topic, and a ground-truth answer derived directly from the information in the document .
  • The paper aims to address the challenge of evaluating language models on test splits that may have leaked into the training set, which can lead to misleading conclusions. By providing a new test dataset like REPLIQA, it fosters sound evaluation of language models .
  • It contributes to the field of natural language processing by providing a benchmark dataset that helps in assessing the ability of language models to generalize to novel samples, ensuring the validity of conclusions drawn from model evaluations .

What work can be continued in depth?

To delve deeper into the work outlined in the provided context, further exploration can focus on the following aspects:

  • Evaluation of Language Models: The assessment of popular state-of-the-art Large Language Models (LLMs) to determine their ability to comprehend provided contexts and accurately respond to user queries, known as question answering. This evaluation involves testing the models on metrics such as F1 score and recall, as well as their capability to detect and handle unanswerable questions .
  • Benchmarking LLMs: The benchmarking of various widely-used LLMs, including GPT-3.5, GPT-4O, LLAMA 3, GEMINI 1.0 and 1.5, WIZARDLM, MISTRAL, MIXTRAL, COMMAND R, COMMAND R+, ARCTIC, and others, to assess their performance in reading comprehension and topic retrieval tasks. This benchmarking process utilizes a unified framework called OPENROUTER for inference .
  • Dataset Creation and Maintenance: The ongoing maintenance and enhancement of the REPLIQA dataset for benchmarking LLMs on unseen reference content. This involves considering incoming recommendations, updating the dataset accordingly, and ensuring its active maintenance until at least the end of 2025. The dataset comprises a substantial number of question-answer pairs based on diverse reference documents .
  • Content Creation Guidelines: The adherence to specific guidelines for creating high-quality content, including conducting thorough research, ensuring factual accuracy, incorporating originality and creativity, using diverse and inclusive content, following legal and ethical standards, and engaging in document review and revision processes. These guidelines aim to produce informative, engaging, and accurate content for evaluation purposes .
Tables
1
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