Search Results for author:
Found 21 papers, 10 papers with code
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts.
Ranked #3 on
Table-based Fact Verification
on TabFact
Universal Self-Adaptive Prompting
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.
Selectively Answering Ambiguous Questions
We investigate question answering from this perspective, focusing on answering a subset of questions with a high degree of accuracy, from a set of questions in which many are inherently ambiguous.
DIFFQG: Generating Questions to Summarize Factual Changes
We propose representing factual changes between paired documents as question-answer pairs, where the answer to the same question differs between two versions.
DePlot: One-shot visual language reasoning by plot-to-table translation
Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24. 0% improvement over finetuned SOTA on human-written queries from the task of chart QA.
Chart Question Answering
Factual Inconsistency Detection in Chart Captioning
+3
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering
Visual language data such as plots, charts, and infographics are ubiquitous in the human world.
Ranked #1 on
Visual Question Answering
on PlotQA-D2
Leveraging Data Recasting to Enhance Tabular Reasoning
Creating challenging tabular inference data is essential for learning complex reasoning.
Table-To-Text generation and pre-training with TabT5
Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS (Herzig et al., 2020).
MiQA: A Benchmark for Inference on Metaphorical Questions
We propose a benchmark to assess the capability of large language models to reason with conventional metaphors.
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour
Language models (LMs) trained on raw texts have no direct access to the physical world.
WinoDict: Probing language models for in-context word acquisition
We introduce a new in-context learning paradigm to measure Large Language Models' (LLMs) ability to learn novel words during inference.
MATE: Multi-view Attention for Table Transformer Efficiency
However, more than 20% of relational tables on the web have 20 or more rows (Cafarella et al., 2008), and these large tables present a challenge for current Transformer models, which are typically limited to 512 tokens.
Ranked #2 on
Question Answering
on HybridQA
Time-Aware Language Models as Temporal Knowledge Bases
We introduce a diagnostic dataset aimed at probing LMs for factual knowledge that changes over time and highlight problems with LMs at either end of the spectrum -- those trained on specific slices of temporal data, as well as those trained on a wide range of temporal data.
DoT: An efficient Double Transformer for NLP tasks with tables
To improve efficiency while maintaining a high accuracy, we propose a new architecture, DoT, a double transformer model, that decomposes the problem into two sub-tasks: A shallow pruning transformer that selects the top-K tokens, followed by a deep task-specific transformer that takes as input those K tokens.
Fool Me Twice: Entailment from Wikipedia Gamification
We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game.
TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training
We adopt the binary TAPAS model of Eisenschlos et al. (2020) to this task.
Open Domain Question Answering over Tables via Dense Retrieval
Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages.
Understanding tables with intermediate pre-training
To be able to use long examples as input of BERT models, we evaluate table pruning techniques as a pre-processing step to drastically improve the training and prediction efficiency at a moderate drop in accuracy.
Ranked #9 on
Table-based Fact Verification
on TabFact
SoftSort: A Continuous Relaxation for the argsort Operator
While sorting is an important procedure in computer science, the argsort operator - which takes as input a vector and returns its sorting permutation - has a discrete image and thus zero gradients almost everywhere.
TAPAS: Weakly Supervised Table Parsing via Pre-training
In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms.
Ranked #1 on
Semantic Parsing
on SQA
(Accuracy metric)
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data.
Ranked #1 on
Zero-shot Cross-Lingual Document Classification
on Cross-Lingual Sentiment (CLS)- English to German - DVD
Cross-Lingual Document Classification
Document Classification
+2
