2 citations · 4 across the 6 of their papers we have counts for
6 papers
A Controlled Study on Long Context Extension and Generalization in LLMs
Yi Lu, Jing Nathan Yan, Songlin Yang +6
Broad textual understanding and in-context learning require language models that utilize full document contexts. Due to the implementation challenges associated with directly train…
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text
Wenting Zhao, Ye Liu, Tong Niu +5
Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when solely relying on their internal knowledge, especially when…
Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tables
Wenting Zhao, Ye Liu, Yao Wan +3
Question answering on tabular data (a.k.a TableQA), which aims at generating answers to questions grounded on a provided table, has gained significant attention recently. Prior wor…
Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations
Wenting Zhao, Justin T. Chiu, Claire Cardie +1
Abductive reasoning aims to find plausible explanations for an event. This style of reasoning is critical for commonsense tasks where there are often multiple plausible explanation…
HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale Supervision
Wenting Zhao, Justin T. Chiu, Claire Cardie +1
Explainable multi-hop question answering (QA) not only predicts answers but also identifies rationales, i. e. subsets of input sentences used to derive the answers. This problem ha…
Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots
Wenting Zhao, Ye Liu, Yao Wan +1
Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data. Despite many efforts having been made towards gen…