# TimelineQA: A Benchmark for Question Answering over Timelines

arXiv: https://arxiv.org/abs/2306.01069 · HTML: https://mlsanity.com/paper/2306.01069/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://mlsanity.com/paper/2107.07653/) (2021)
- [Database Reasoning Over Text](https://mlsanity.com/paper/2106.01074/) (2021)

## Built on by (most cited first, 10 of 10)

- [Multimodal Graph Learning for Generative Tasks](https://mlsanity.com/paper/2310.07478/) (2023)
- [TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models](https://mlsanity.com/paper/2405.18027/) (2024)
- [LifeBench: A Benchmark for Long-Horizon Multi-Source Memory](https://mlsanity.com/paper/2603.03781/) (2026)
- [DynamicMem: A Long-Horizon Memory Benchmark in Real-World Settings](https://mlsanity.com/paper/2606.22877/) (2026)
- [LLMLagBench: Identifying Temporal Training Boundaries in Large Language Models](https://mlsanity.com/paper/2511.12116/) (2025)
- [When Facts Change: Probing LLMs on Evolving Knowledge with evolveQA](https://mlsanity.com/paper/2510.19172/) (2025)
- [Recursive Question Understanding for Complex Question Answering over Heterogeneous Personal Data](https://mlsanity.com/paper/2505.11900/) (2025)
- [The ReQAP System for Question Answering over Personal Information](https://mlsanity.com/paper/2508.06880/) (2025)
- [Life-Bench: A Benchmark and Knowledge Graph Framework for Multimodal Personalization Beyond Concept Recognition](https://mlsanity.com/paper/2602.19001/) (2026)
- [PAL-Bench: Evidence-Grounded Profile Reconstruction from Longitudinal Personal Albums](https://mlsanity.com/paper/2606.16175/) (2026)

## Abstract (arXiv)

Lifelogs are descriptions of experiences that a person had during their life. Lifelogs are created by fusing data from the multitude of digital services, such as online photos, maps, shopping and content streaming services. Question answering over lifelogs can offer personal assistants a critical resource when they try to provide advice in context. However, obtaining answers to questions over lifelogs is beyond the current state of the art of question answering techniques for a variety of reasons, the most pronounced of which is that lifelogs combine free text with some degree of structure such as temporal and geographical information. We create and publicly release TimelineQA1, a benchmark for accelerating progress on querying lifelogs. TimelineQA generates lifelogs of imaginary people. The episodes in the lifelog range from major life episodes such as high school graduation to those that occur on a daily basis such as going for a run. We describe a set of experiments on TimelineQA with several state-of-the-art QA models. Our experiments reveal that for atomic queries, an extractive QA system significantly out-performs a state-of-the-art retrieval-augmented QA system. For multi-hop queries involving aggregates, we show that the best result is obtained with a state-of-the-art table QA technique, assuming the ground truth set of episodes for deriving the answer is available.