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 …

Recent breakthroughs in large language modeling have facilitated rigorous exploration of
their application in diverse tasks related to tabular data modeling, such as prediction, tabular …

In this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-
augmented generation (RAG) and conversational question answering (QA). To enhance …

As Large Language Models (LLMs) become popular, there emerged an important trend of
using multimodality to augment the LLMs' generation ability, which enables LLMs to better …

To facilitate conversational question answering (CQA) over hybrid contexts in finance, we
present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC …

Recent breakthroughs in large language modeling have facilitated rigorous exploration of
their application in diverse tasks related to tabular data modeling, such as prediction, tabular …

In this work, we introduce ChatQA, a family of conversational question answering (QA)
models, that obtain GPT-4 level accuracies. Specifically, we propose a two-stage instruction …

As businesses, products, and services spring up around large language models, the
trustworthiness of these models hinges on the verifiability of their outputs. However, methods …

Optimizing accuracy and performance while eliminating hallucinations of open-domain
conversational large language models (LLMs) is an open research challenge. A particularly …

While language model (LM)-powered chatbots and generative search engines excel at
answering concrete queries, discovering information in the terrain of unknown unknowns …