Qa dataset explosion: A taxonomy of nlp resources for question answering and reading comprehension
Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …
there has been much work on benchmark datasets needed to track modeling progress …
A survey of deep learning for mathematical reasoning
Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in
various fields, including science, engineering, finance, and everyday life. The development …
various fields, including science, engineering, finance, and everyday life. The development …
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Recently, there has been significant progress in teaching language models to perform step-
by-step reasoning to solve complex numerical reasoning tasks. Chain-of-thoughts prompting …
by-step reasoning to solve complex numerical reasoning tasks. Chain-of-thoughts prompting …
Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls
Text-to-SQL parsing, which aims at converting natural language instructions into executable
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …
Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
Mathematical reasoning, a core ability of human intelligence, presents unique challenges for
machines in abstract thinking and logical reasoning. Recent large pre-trained language …
machines in abstract thinking and logical reasoning. Recent large pre-trained language …
Theoremqa: A theorem-driven question answering dataset
The recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving
fundamental math problems like GSM8K by achieving over 90% accuracy. However, their …
fundamental math problems like GSM8K by achieving over 90% accuracy. However, their …
Pixiu: A large language model, instruction data and evaluation benchmark for finance
Q Xie, W Han, X Zhang, Y Lai, M Peng… - arXiv preprint arXiv …, 2023 - arxiv.org
Although large language models (LLMs) has shown great performance on natural language
processing (NLP) in the financial domain, there are no publicly available financial tailtored …
processing (NLP) in the financial domain, there are no publicly available financial tailtored …
[HTML][HTML] A survey of GPT-3 family large language models including ChatGPT and GPT-4
KS Kalyan - Natural Language Processing Journal, 2023 - Elsevier
Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …
Large language models are few (1)-shot table reasoners
W Chen - arXiv preprint arXiv:2210.06710, 2022 - arxiv.org
Recent literature has shown that large language models (LLMs) are generally excellent few-
shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table …
shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table …
MultiHiertt: Numerical reasoning over multi hierarchical tabular and textual data
Numerical reasoning over hybrid data containing both textual and tabular content (eg,
financial reports) has recently attracted much attention in the NLP community. However …
financial reports) has recently attracted much attention in the NLP community. However …