# Automatic Question-Answer Generation for Long-Tail Knowledge

arXiv: https://arxiv.org/abs/2403.01382 · HTML: https://mlsanity.com/paper/2403.01382/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://mlsanity.com/paper/1704.05179/) (2017)
- [GreaseLM: Graph REASoning Enhanced Language Models for Question Answering](https://mlsanity.com/paper/2201.08860/) (2022)
- [JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering](https://mlsanity.com/paper/2112.02732/) (2021)
- [Memorisation versus Generalisation in Pre-trained Language Models](https://mlsanity.com/paper/2105.00828/) (2021)
- [SLING: A framework for frame semantic parsing](https://mlsanity.com/paper/1710.07032/) (2017)

## Built on by (most cited first, 8 of 8)

- [Are Large Language Models a Good Replacement of Taxonomies?](https://mlsanity.com/paper/2406.11131/) (2024)
- [ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty](https://mlsanity.com/paper/2412.20251/) (2024)
- [LPFQA: A Long-Tail Professional Forum-based Benchmark for LLM Evaluation](https://mlsanity.com/paper/2511.06346/) (2025)
- [Augmenting Textual Generation via Topology Aware Retrieval](https://mlsanity.com/paper/2405.17602/) (2024)
- [Fact or Guesswork? Evaluating Large Language Models' Medical Knowledge with Structured One-Hop Judgments](https://mlsanity.com/paper/2502.14275/) (2025)
- [A Multi-Stage Framework with Taxonomy-Guided Reasoning for Occupation Classification Using Large Language Models](https://mlsanity.com/paper/2503.12989/) (2025)
- [Are LLMs Really Not Knowledgeable? Mining the Submerged Knowledge in LLMs' Memory](https://mlsanity.com/paper/2412.20846/) (2024)
- [TailNLG: A Multilingual Benchmark Addressing Verbalization of Long-Tail Entities](https://mlsanity.com/paper/2603.27768/) (2026)

## Abstract (arXiv)

Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA). While they exhibit high accuracy in answering questions related to common knowledge, LLMs encounter difficulties in learning about uncommon long-tail knowledge (tail entities). Since manually constructing QA datasets demands substantial human resources, the types of existing QA datasets are limited, leaving us with a scarcity of datasets to study the performance of LLMs on tail entities. In this paper, we propose an automatic approach to generate specialized QA datasets for tail entities and present the associated research challenges. We conduct extensive experiments by employing pretrained LLMs on our newly generated long-tail QA datasets, comparing their performance with and without external resources including Wikipedia and Wikidata knowledge graphs.