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Found 27 papers, 18 papers with code
Focus on the Action: Learning to Highlight and Summarize Jointly for Email To-Do Items Summarization
Automatic email to-do item generation is the task of generating to-do items from a given email to help people overview emails and schedule daily work.
Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization
Abstractive conversation summarization has received growing attention while most current state-of-the-art summarization models heavily rely on human-annotated summaries.
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach
Using COVID-19 as a testbed domain, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach.
Unlearn What You Want to Forget: Efficient Unlearning for LLMs
Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data, however, this process might suffer from privacy issues and violations of data protection regulations.
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks.
Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models
Compositional generalization empowers the LLMs to solve problems that are harder than the ones they have seen (i. e., easy-to-hard generalization), which is a critical reasoning capability of human-like intelligence.
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Can Large Language Models Transform Computational Social Science?
We conclude that the performance of today's LLMs can augment the CSS research pipeline in two ways: (1) serving as zero-shot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e. g., explaining the underlying attributes of a text).
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise
Diffusion models that are based on iterative denoising have been recently proposed and leveraged in various generation tasks like image generation.
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i. e., without adaptation on downstream data.
Parameter-Efficient Fine-Tuning Design Spaces
We discover the following design patterns: (i) group layers in a spindle pattern; (ii) allocate the number of trainable parameters to layers uniformly; (iii) tune all the groups; (iv) assign proper tuning strategies to different groups.
Human-in-the-loop Abstractive Dialogue Summarization
Specifically, we ask humans to highlight the salient information to be included in summaries to provide the local feedback , and to make overall comparisons among summaries in terms of coherence, accuracy, coverage, concise and overall quality, as the global feedback.
WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain.
VALUE: Understanding Dialect Disparity in NLU
To understand disparities in current models and to facilitate more dialect-competent NLU systems, we introduce the VernAcular Language Understanding Evaluation (VALUE) benchmark, a challenging variant of GLUE that we created with a set of lexical and morphosyntactic transformation rules.
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition
We found that state-of-the-art NER systems trained on CoNLL 2003 training data drop performance dramatically on our challenging set.
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability
Fine-tuning large pre-trained models with task-specific data has achieved great success in NLP.
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP
NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets.
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization
Fine-tuning large pre-trained models with task-specific data has achieved great success in NLP.
Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs
Abstractive conversation summarization has received much attention recently.
Continual Learning for Text Classification with Information Disentanglement Based Regularization
Continual learning has become increasingly important as it enables NLP models to constantly learn and gain knowledge over time.
Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests
Modeling persuasive language has the potential to better facilitate our decision-making processes.
Examining the Ordering of Rhetorical Strategies in Persuasive Requests
We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request.
Local Additivity Based Data Augmentation for Semi-supervised NER
Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data.
Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization
Text summarization is one of the most challenging and interesting problems in NLP.
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix.
Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses
We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses.
Let's Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms
Modeling what makes a request persuasive - eliciting the desired response from a reader - is critical to the study of propaganda, behavioral economics, and advertising.
Incorporating Structured Commonsense Knowledge in Story Completion
The ability to select an appropriate story ending is the first step towards perfect narrative comprehension.
