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Found 17 papers, 14 papers with code
DocMath-Eval: Evaluating Numerical Reasoning Capabilities of LLMs in Understanding Long Documents with Tabular Data
This paper introduces DocMath-Eval, a comprehensive benchmark specifically designed to evaluate the numerical reasoning and problem-solving capabilities of LLMs in the context of understanding and analyzing financial documents containing both text and tables.
On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering
A key discovery is the identification of two primary bottlenecks hindering effective interaction: the capacity for planning and the ability to generate multiple SQL queries.
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task-specific perturbations, e. g., replacing key question entities or shuffling table columns.
Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios
These include the LogicNLG and our newly-constructed LoTNLG datasets for data insight generation, along with the FeTaQA and our newly-constructed F2WTQ datasets for query-based generation.
QTSumm: Query-Focused Summarization over Tabular Data
Motivated by this, we define a new query-focused table summarization task, where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored summary.
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies
In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context that has been supplemented with a few examples or task-specific instructions.
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control
Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables.
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation
Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests.
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills.
Ranked #3 on
Semantic Parsing
on WikiSQL
(Denotation accuracy (test) metric)
FOLIO: Natural Language Reasoning with First-Order Logic
We present FOLIO, a human-annotated, open-domain, and logically complex and diverse dataset for reasoning in natural language (NL), equipped with first order logic (FOL) annotations.
Leveraging Locality in Abstractive Text Summarization
Our experimental results show that our model has a better performance compared with strong baselines with efficient attention modules, and our analysis provides further insights into our locality-aware modeling strategy.
R2D2: Robust Data-to-Text with Replacement Detection
Unfaithful text generation is a common problem for text generation systems.
FeTaQA: Free-form Table Question Answering
Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to include questions that require complex reasoning and integration of information due to the constraint of the associated short-form answers.
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages
We adopt cross-lingual embeddings constructed using different methods to extract features of the tweets, including a few state-of-the-art contextual embeddings such as BERT, RoBERTa and XLM-R. We train classifiers of different architectures on the extracted features.
DART: Open-Domain Structured Data Record to Text Generation
Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures.
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates.
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures
In the spirit of reproducible research we make our data, models and code available, aiming to set a gold standard for purity of training and testing sets.
