Analyzing Question Answering Datasets

In this article, we look at different Datasets used for our research. The information includes details such as the number of samples, the number of questions, and hints provided in each dataset.

#Dataset Details

We examined three main datasets: TriviaQA, NQ, and WebQ. Here are the details regarding the datasets.

#Question Type Distribution

The distribution of question types in the datasets is also crucial for our study.

#Metrics Used

In this section, we discuss the metrics used to evaluate the methods in our research. The scikit-learn library helped us calculate these metrics.

#Accuracy (ACC)

This metric checks if the answers given by the model are correct.

#Exact Match (EM)

This measures if the retrieved or generated passage includes the correct answer exactly as it appears.

#Precision (PR)

This shows how many words in the passage are also found in the correct answer.

#Recall (RC)

This calculates the number of words from the correct answer that are included in the passage retrieved.

#F1-measure (F1)

This is a balance between precision and recall.

#Contains (CON)

This metric checks if the retrieved passage has the entire correct answer.

#BERTScore (BERT)

This metric checks how similar the words in the retrieved passage are to the answer by using word embeddings from BERT.

#Experimental Results

In this section, we provide results from our experiments in various scenarios. We examine how different conditions and factors affect the results.

The number of hints gives context, while the ranking column lists methods for reranking these hints.

#Results for TriviaQA Dataset

In our experiments, we analyzed the results of using T5-3b as the reader. We applied both zero-shot and few-shot learning strategies on the TriviaQA dataset.

Here are the results based on different ranking methods and hint amounts.

#NQ Dataset Results

Similar to the previous dataset, we provide results for the NQ dataset using T5-3b under zero-shot and few-shot conditions.

#WebQ Dataset Results

Lastly, we present results for the WebQ dataset under the same conditions.

#Performance of T5-3b and LLaMA-7b

We also examined the performance of T5-3b and LLaMA-7b in various scenarios using different hint methods.

#Case Studies

In this section, we display several case studies that demonstrate the prompts we chose, along with examples from our experiments.

#Sample Questions and Answers

Here are some sample questions from the datasets, along with how the models responded:

#Hints Generated

Our case studies illustrate how hints were generated for various questions. Each hint provided context to help the models find the correct answers.

#Conclusion

In this article, we explored various datasets used for our research, focusing on their details, the metrics used for evaluation, experimental results, and case studies that illustrate the practical application of our methods. The goal is to contribute to the understanding of how different models perform in answering questions with the aid of contextual hints.