Reasoning like program executors
Reasoning over natural language is a long-standing goal for the research community.
However, studies have shown that existing language models are inadequate in reasoning …
However, studies have shown that existing language models are inadequate in reasoning …
Docmath-eval: Evaluating math reasoning capabilities of llms in understanding financial documents
Recent LLMs have demonstrated remarkable performance in solving exam-like math word
problems. However, the degree to which these numerical reasoning skills are effective in …
problems. However, the degree to which these numerical reasoning skills are effective in …
PACIFIC: towards proactive conversational question answering over tabular and textual data in finance
To facilitate conversational question answering (CQA) over hybrid contexts in finance, we
present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC …
present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC …
KnowledgeFMath: A Knowledge-Intensive Math Reasoning Dataset in Finance Domains
We introduce KnowledgeFMath, a novel benchmark designed to evaluate LLMs' capabilities
in solving knowledge-intensive math reasoning problems. Compared to prior works, this …
in solving knowledge-intensive math reasoning problems. Compared to prior works, this …
Answering numerical reasoning questions in table-text hybrid contents with graph-based encoder and tree-based decoder
In the real-world question answering scenarios, hybrid form combining both tabular and
textual contents has attracted more and more attention, among which numerical reasoning …
textual contents has attracted more and more attention, among which numerical reasoning …
Towards complex document understanding by discrete reasoning
Document Visual Question Answering (VQA) aims to answer questions over visually-rich
documents. In this work, we introduce a new Document VQA dataset, named TAT-DQA …
documents. In this work, we introduce a new Document VQA dataset, named TAT-DQA …
Menatqa: A new dataset for testing the temporal comprehension and reasoning abilities of large language models
Large language models (LLMs) have shown nearly saturated performance on many natural
language processing (NLP) tasks. As a result, it is natural for people to believe that LLMs …
language processing (NLP) tasks. As a result, it is natural for people to believe that LLMs …
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. Current models with table-specific architectures and pre-training …
table reasoning skills. Current models with table-specific architectures and pre-training …
Docmath-eval: Evaluating numerical reasoning capabilities of llms in understanding long documents with tabular data
Recent LLMs have demonstrated remarkable performance in solving exam-like math word
problems. However, the degree to which these numerical reasoning skills are effective in …
problems. However, the degree to which these numerical reasoning skills are effective in …
Tat-llm: A specialized language model for discrete reasoning over tabular and textual data
In this work, we address question answering (QA) over a hybrid of tabular and textual data
that are very common content on the Web (eg SEC filings), where discrete reasoning …
that are very common content on the Web (eg SEC filings), where discrete reasoning …