TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions

TriviaHG is an extensive dataset crafted specifically for hint generation in question answering. Unlike conventional datasets, TriviaHG provides 10 hints per question instead of direct answers. This unique approach encourages users to engage in critical thinking and reasoning to derive the solution. Covering diverse question types across varying difficulty levels, the dataset is partitioned into training, validation, and test sets. These subsets facilitate the fine-tuning and training of large language models, enhancing the generation of high-quality hints.

Dataset

TriviaHG comprises several sub-datasets, each encompassing ⬇️Training, ⬇️Validation, and ⬇️Test sets. You can access and download each subset by clicking on its respective link.

The dataset is structured as JSON files, including training.json, validation.json, and test.json for training, validation, and test phases, respectively:

[
    {
        "Q_ID": "",
        "Question": "",
        "Hints": [ ],
        "Hints_Sources": [  ],
        "Snippet": "",
        "Snippet_Sources": [  ],
        "ExactAnswer": [  ],
        "MajorType": "",
        "MinorType": "",
        "Candidates_Answers": [  ],
        "Q_Popularity": {  },
        "Exact_Answer_Popularity": {  },
        "H_Popularity": [  ],
        "Scores": [  ],
        "Convergence": [  ],
        "Familiarity": [  ]
    }
]

Dataset Statistics

	Training	Validation	Test
Num. of Questions	14,645	1,000	1,000
Num. of Hints	140,973	9,638	9,619

Framework and Model Deployment

The Framework directory houses essential files for the hint generation framework. Notably, you will find Framework.ipynb, a Jupyter Notebook tailored for executing and exploring the framework's code. Utilize 🌐Google Colab to seamlessly run this notebook and delve into the hint generation process.

Finetuned Language Models

We have finetuned several large language models, including LLaMA 7b, LLaMA 13b, and LLaMA 70b, on the TriviaHG dataset. These models are not available for direct download but can be accessed via API functions provided by AnyScale.com. Below are the IDs for the finetuned models:

• LLaMA 7b Finetuned: meta-llama/Llama-2-7b-chat-hf:Hint_Generator:X6odC0D
• LLaMA 13b Finetuned: meta-llama/Llama-2-13b-chat-hf:Hint_Generator:ajid9Dr
• LLaMA 70b Finetuned: meta-llama/Llama-2-70b-chat-hf:Hint_Generator:NispySP

Querying Finetuned Models

Using CURL:

export ENDPOINTS_AUTH_TOKEN=YOUR_API_KEY

curl "https://api.endpoints.anyscale.com/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $ENDPOINTS_AUTH_TOKEN" \
  -d '{
    "model": "meta-llama/Llama-2-70b-chat-hf:Hint_Generator:NispySP",
    "messages": [
      {"role": "user", "content": "Generate 10 hints for the following question. Question: Which country has the highest population?"}
    ],
    "temperature": 0.0
  }'

Or using Python:

import os
import requests

s = requests.Session()

api_base = "https://api.endpoints.anyscale.com/v1"
# Replace with long-lived credentials for production
token = YOUR_API_KEY
url = f"{api_base}/chat/completions"
body = {
  "model": "meta-llama/Llama-2-70b-chat-hf:Hint_Generator:NispySP",
  "messages": [
    {"role": "user", "content": "Generate 10 hints for the following question. Question: Which country has the highest population?"}
  ],
  "temperature": 0.0
}

with s.post(url, headers={"Authorization": f"Bearer {token}"}, json=body) as resp:
  print(resp.json())

Evaluation

Human Evaluation - Answering

The Human Evaluation - Answering folder is a repository that houses Excel files utilized to gather responses from six human participants. Each participant was assigned ten distinct Excel files, each containing a set of ten questions. The table below outlines the types of questions included in the Excel files, along with corresponding statistics collected from participants. The columns in the table below adhere to the format {Difficulty}-{Model}, where B, F, and V represent Bing, LLaMA 7b Finetuned, and LLaMA 7b Vanilla, respectively.

Question Type	Hard-B	Hard-F	Hard-V	Medium-B	Medium-F	Medium-V	Easy-B	Easy-F	Easy-V
ENTITY	5 / 9	5 / 9	4 / 9	8 / 8	6 / 8	4 / 8	8 / 8	8 / 8	6 / 8
HUMAN	2 / 9	0 / 9	0 / 9	5 / 8	1 / 8	0 / 8	6 / 8	6 / 8	4 / 8
LOCATION	0 / 9	0 / 9	0 / 9	7 / 8	5 / 8	2 / 8	7 / 8	6 / 8	4 / 8
OTHER	3 / 9	2 / 9	0 / 9	5 / 8	2 / 8	0 / 8	8 / 8	7 / 8	7 / 8

Human Evaluation - Quality

The Human Evaluation - Quality folder encompasses ten Excel files, each containing human annotation values assigned to 2,791 hints across various quality attributes such as relevance, readability, ambiguity, convergence, and familiarity. These attributes are essential markers in assessing the overall quality and effectiveness of the hints generated. The table below provides a concise summary of the average scores attained for each quality attribute, offering insights into the perceived quality of the hints evaluated by human participants.

Method	Match	Readability	Ambiguity	Convergence	Familiarity
Copilot	4.09	4.67	1.51	2.23	2.47
LLaMA 7b - Finetuned	4.01	4.70	1.56	2.20	2.41
LLaMA 7b - Vanilla	3.64	4.47	1.87	2.12	2.02

Model Performance

Within the Model Performance folder, comprehensive insights into the generated hints and their evaluation values for convergence (HICOS) and familiarity (HIFAS) quality attributes are provided. The table below presents a comparative analysis of the results obtained from various models, shedding light on their respective performances in terms of HICOS and HIFAS. This comparative assessment is a valuable resource for gauging the efficacy and effectiveness of each model's hint generation capabilities, thereby informing further enhancements and refinements in the generation process.

Model	HICOS	HIFAS
LLaMA_7b_Vanilla	0.307	0.833
LLaMA_13b_Vanilla	0.350	0.929
LLaMA_7b_Finetuned	0.400	0.890
LLaMA_13b_Finetuned	0.410	0.881
LLaMA_70b_Vanilla	0.425	0.941
GPT_3.5	0.438	0.911
WizardLM_70b	0.446	0.942
Gemini	0.455	0.911
LLaMA_70b_Finetuned	0.494	0.862
GPT_4_turbo	0.525	0.875
Copilot	0.540	0.946

Entities

The Entities folder contains a JSON file with 50,000 entities utilized in the Interquartile Range (IQR) method to determine Q1 and Q3 for normalization purposes. These entities play a crucial role in statistical normalization, ensuring robust data distributions and accurate analytical insights. Leveraging the IQR method with this extensive entity set enables users to effectively manage variations and outliers, enhancing the accuracy and reliability of analyses across various domains.

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