System: You are a helpful and precise assistant in checking the quality of the answer.
User: We would like to request your feedback on the performance of the response of the assistant to the user instruction displayed below. In the feedback, I want you to rate the quality of the response in these 3 categories according to each scoring rubric:

Factuality: Did the model extract pertinent and accurate background knowledge without any misinformation when factual knowledge retrieval is needed? Is the response supported by reliable evidence or citation of the source of its information?
Score 1: The model did not extract pertinent background knowledge and provided inaccurate or misleading information. There is no support for the response through reliable evidence or source citations.
Score 2: The model extracted some relevant background knowledge but included inaccuracies or incomplete information. The response has minimal support through evidence or citations, with questionable reliability.
Score 3: The model extracted generally accurate and pertinent background knowledge, with minor inaccuracies or omissions. The response is partially supported by evidence or citations, but the support may not be comprehensive or fully reliable.
Score 4: The model extracted mostly accurate and relevant background knowledge but missed minor evidence or citations to support the response.
Score 5: The model extracted complete and accurate background knowledge without any misinformation. The response is fully supported by reliable evidence or citations that are accurate, relevant, and comprehensive in addressing the instruction.
Comprehension: Does the response fulfill the requirements of the instruction by providing relevant information especially when the instruction is complex and includes multiple requirements? This includes responding in accordance with the explicit and implicit purpose of given instruction.
Score 1: The response is completely unrelated to the instruction, or the model entirely misunderstands the instruction.
Score 2: Most of the key points in the response are irrelevant to the instruction, and the response misses major requirements of the instruction.
Score 3: Some major points in the response contain irrelevant information or miss some requirements of the instruction.
Score 4: The response is relevant to the instruction but misses minor requirements of the instruction.
Score 5: The response is perfectly relevant to the instruction, and the model fulfills all of the requirements of the instruction.
Conciseness: Is the response presented in a concise manner for the reader without any unnecessary information?
Score 1: The response is highly redundant or contains a lot of unnecessary information, requiring a complete rewrite for optimal clarity and efficiency.
Score 2: The response lacks conciseness and needs a substantial rewrite for better optimization.
Score 3: The response is somewhat concise but includes unnecessary information, requiring some edits for improved optimization.
Score 4: The response is mostly concise but could benefit from minor edits for better optimization.
Score 5: The response is optimally concise and does not contain any unnecessary information, requiring no further optimization.
[Instruction]
You need to come up with the names of some of the papers that relate to the paper you are given.
Input: RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering

[Ground truth Answer]
+ Question Answering Infused Pre-training of General-Purpose Contextualized Representations
+ Zero-Shot Dialogue State Tracking via Cross-Task Transfer
+ Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
+ HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
+ FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models
+ MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
+ FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark
+ Multilingual Transfer Learning for QA Using Translation as Data Augmentation
+ Towards Zero Shot Conditional Summarization with Adaptive Multi-task Fine-Tuning

[Assistant's Response]
To find related papers to "RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering," it is helpful to explore various aspects of question answering (QA) that align with the themes of robustness, multi-evidence, and multi-answer scenarios. Here’s a structured overview of relevant literature across these domains:

### 1. **Question Answering Benchmarks and Datasets**
   - **SQuAD**: "SQuAD: 100,000+ Questions for Machine Comprehension of Text" - A widely used benchmark for evaluating reading comprehension.
   - **TriviaQA**: "TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension" - Focuses on diverse trivia questions sourced from the web.
   - **Natural Questions**: "Natural Questions: A Benchmark for Question Answering Research" - Provides a large dataset of real user queries with answers from Wikipedia.

### 2. **Multi-evidence Question Answering**
   - **MultiRC**: "MultiRC: A Dataset for Multi-Sentence Reading Comprehension" - Designed for evaluating models on multi-sentence reasoning.
   - **HotpotQA**: "HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering" - Involves answering questions that require information from multiple sources.

### 3. **Robustness in Question Answering**
   - **Adversarial Examples**: "Adversarial Examples for Evaluating Reading Comprehension Systems" - Discusses the impact of adversarial inputs on QA systems.
   - **RobustQA**: "RobustQA: A Robust Question Answering Benchmark" - Introduces a benchmark focused on the robustness of QA systems against various challenges.

### 4. **Multi-answer Question Answering**
   - **QuAC**: "QuAC: Question Answering in Context" - A dataset that emphasizes conversational QA, allowing for multiple answers based on context.
   - **CoQA**: "CoQA: A Conversational Question Answering Challenge" - Focuses on conversational context and multi-turn interactions in QA.

### 5. **Advancements in Model Architectures**
   - **BERT**: "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" - A foundational paper that has influenced many QA systems.
   - **T5**: "T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" - Discusses a unified framework for various NLP tasks, including QA.

These papers collectively provide a comprehensive view of the current landscape in question answering research and can help contextualize the contributions of the RoMQA benchmark. They cover essential datasets, methodologies for multi-evidence and multi-answer scenarios, and considerations for robustness, making them highly relevant for further exploration in this field.

[The End of Assistant's Response]

Please give feedback on the assistant's responses. Also, provide the assistant with a score on a scale of 1 to 5 for each category, where a higher score indicates better overall performance. Make sure to give feedback or comments for each category first and then write the score for each category. Only write the feedback corresponding to the scoring rubric for each category. The scores of each category should be orthogonal, indicating that 'Efficiency of User Alignment' should not be considered for 'Readability of User Alignment' category, for example. 

Lastly, return a Python dictionary object that has skillset names as keys and the corresponding scores as values.

[System]



Assistant: