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Julian Martin Eisenschlos

6 accepted papers

2025

WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts

Negar Foroutan, Angelika Romanou, Matin Ansaripour, Julian Martin Eisenschlos, Karl Aberer, Rémi Lebret
ACL 2025finding

Documents are fundamental to preserving and disseminating information, often incorporating complex layouts, tables, and charts that pose significant challenges for automatic document understanding (DU). While vision-language large models (VLLMs) have demonstrated improvements across various tasks, t…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2024

Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding

Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang +5
ICLR 2024poster

Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both fr…

Cited by 107Source ↗PDF ↗Scholar ↗
2024

TableRAG: Million-Token Table Understanding with Language Models

Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen +4
NeurIPS 2024poster

Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables. However, these methods often require the entire table as input, leading to scalability challenges due to the po…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2023

Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding

Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu, Julian Martin Eisenschlos +4
ICML 2023oral

Visually-situated language is ubiquitous---sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domain-specific recipes with limited sharing of the underlying data,…

Cited by 295Source ↗PDF ↗code ↗Scholar ↗
2023

Selectively Answering Ambiguous Questions

Jeremy R. Cole, Michael JQ Zhang, Daniel Gillick, Julian Martin Eisenschlos, Bhuwan Dhingra, Jacob Eisenstein
EMNLP 2023long main

Trustworthy language models should abstain from answering questions when they do not know the answer. However, the answer to a question can be unknown for a variety of reasons. Prior research has focused on the case in which the question is clear and the answer is unambiguous but possibly unknown.…

Cited by 0Source ↗Scholar ↗
2023

Universal Self-Adaptive Prompting

Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan O Arik +1
EMNLP 2023long main

A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through in-context learning (ICL) via prompting. However, while highly coveted and being the most general, zero-shot performances in LLMs are still typically weaker due to t…

Cited by 0Source ↗Scholar ↗