6 citations · 13 across the 7 of their papers we have counts for
13 papers
Leveraging Data Recasting to Enhance Tabular Reasoning
Aashna Jena, Vivek Gupta, Manish Shrivastava +1
Creating challenging tabular inference data is essential for learning complex reasoning. Prior work has mostly relied on two data generation strategies. The first is human annotati…
Table-To-Text generation and pre-training with TabT5
Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno +2
Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS (Herzig et al., 2020). A major limitation of these architectures…
MiQA: A Benchmark for Inference on Metaphorical Questions
Iulia-Maria Comsa, Julian Martin Eisenschlos, Srini Narayanan
We propose a benchmark to assess the capability of large language models to reason with conventional metaphors. Our benchmark combines the previously isolated topics of metaphor de…
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour
Fangyu Liu, Julian Martin Eisenschlos, Jeremy R. Cole +1
Language models (LMs) trained on raw texts have no direct access to the physical world. Gordon and Van Durme (2013) point out that LMs can thus suffer from reporting bias: texts ra…
WinoDict: Probing language models for in-context word acquisition
Julian Martin Eisenschlos, Jeremy R. Cole, Fangyu Liu +1
We introduce a new in-context learning paradigm to measure Large Language Models' (LLMs) ability to learn novel words during inference. In particular, we rewrite Winograd-style co-…
MATE: Multi-view Attention for Table Transformer Efficiency
Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller +1
This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables. Tables are ubiquitous on the web, and are rich in information. Howe…