A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …
An empirical survey on long document summarization: Datasets, models, and metrics
Long documents such as academic articles and business reports have been the standard
format to detail out important issues and complicated subjects that require extra attention. An …
format to detail out important issues and complicated subjects that require extra attention. An …
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
In the summarization domain, a key requirement for summaries is to be factually consistent
with the input document. Previous work has found that natural language inference (NLI) …
with the input document. Previous work has found that natural language inference (NLI) …
Chatgpt as a factual inconsistency evaluator for text summarization
The performance of text summarization has been greatly boosted by pre-trained language
models. A main concern of existing methods is that most generated summaries are not …
models. A main concern of existing methods is that most generated summaries are not …
QAFactEval: Improved QA-based factual consistency evaluation for summarization
Factual consistency is an essential quality of text summarization models in practical settings.
Existing work in evaluating this dimension can be broadly categorized into two lines of …
Existing work in evaluating this dimension can be broadly categorized into two lines of …
[HTML][HTML] Pre-trained language models with domain knowledge for biomedical extractive summarization
Biomedical text summarization is a critical task for comprehension of an ever-growing
amount of biomedical literature. Pre-trained language models (PLMs) with transformer …
amount of biomedical literature. Pre-trained language models (PLMs) with transformer …
AlignScore: Evaluating factual consistency with a unified alignment function
Many text generation applications require the generated text to be factually consistent with
input information. Automatic evaluation of factual consistency is challenging. Previous work …
input information. Automatic evaluation of factual consistency is challenging. Previous work …
Understanding factual errors in summarization: Errors, summarizers, datasets, error detectors
The propensity of abstractive summarization models to make factual errors has been studied
extensively, including design of metrics to detect factual errors and annotation of errors in …
extensively, including design of metrics to detect factual errors and annotation of errors in …
Submodularity in machine learning and artificial intelligence
J Bilmes - arXiv preprint arXiv:2202.00132, 2022 - arxiv.org
In this manuscript, we offer a gentle review of submodularity and supermodularity and their
properties. We offer a plethora of submodular definitions; a full description of a number of …
properties. We offer a plethora of submodular definitions; a full description of a number of …
SUMMEDITS: measuring LLM ability at factual reasoning through the lens of summarization
With the recent appearance of LLMs in practical settings, having methods that can effectively
detect factual inconsistencies is crucial to reduce the propagation of misinformation and …
detect factual inconsistencies is crucial to reduce the propagation of misinformation and …