47 citations · 95 across the 17 of their papers we have counts for
22 papers · 1 filter
TableRAG: Million-Token Table Understanding with Language Models
Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos +7
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…
Faithful Chart Summarization with ChaTS-Pi
Syrine Krichene, Francesco Piccinno, Fangyu Liu +1
Chart-to-summary generation can help explore data, communicate insights, and help the visually impaired people. Multi-modal generative models have been used to produce fluent summa…
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
Zilong Wang, Hao Zhang, Chun-Liang Li +8
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 verificat…
Universal Self-Adaptive Prompting
Xingchen Wan, Ruoxi Sun, Hootan Nakhost +4
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. Howe…
Selectively Answering Ambiguous Questions
Jeremy R. Cole, Michael J. Q. Zhang, Daniel Gillick +3
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. Pri…
DIFFQG: Generating Questions to Summarize Factual Changes
Jeremy R. Cole, Palak Jain, Julian Martin Eisenschlos +3
Identifying the difference between two versions of the same article is useful to update knowledge bases and to understand how articles evolve. Paired texts occur naturally in diver…