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Found 41 papers, 32 papers with code
{\mathcal{P}^2}: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation
Text verbalization of knowledge graphs is an important problem with wide application to natural language generation (NLG) systems.
Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States
Mining the causes of political decision-making is an active research area in the field of political science.
Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals
Interpretability research aims to bridge the gap between the empirical success and our scientific understanding of the inner workings of large language models (LLMs).
CLadder: Assessing Causal Reasoning in Language Models
Much of the existing work in natural language processing (NLP) focuses on evaluating commonsense causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined formal rules.
Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures
The rapid advancement of Large Language Models (LLMs) has sparked intense debate regarding their ability to perceive and interpret complex socio-political landscapes.
CausalCite: A Causal Formulation of Paper Citations
Evaluating the significance of a paper is pivotal yet challenging for the scientific community.
Can Large Language Models Infer Causation from Correlation?
In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs).
Membership Inference Attacks against Language Models via Neighbourhood Comparison
To investigate whether this fragility provides a layer of safety, we propose and evaluate neighbourhood attacks, which compare model scores for a given sample to scores of synthetically generated neighbour texts and therefore eliminate the need for access to the training data distribution.
Voices of Her: Analyzing Gender Differences in the AI Publication World
While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends.
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP
Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work.
All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations
Transformer models bring propelling advances in various NLP tasks, thus inducing lots of interpretability research on the learned representations of the models.
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models
Not surprisingly, this has, in turn, made many NLP researchers -- especially those at the beginning of their careers -- worry about what NLP research area they should focus on.
OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models
Our study entails finetuning three different sizes of OPT on a carefully curated reasoning corpus, resulting in two sets of finetuned models: OPT-R, finetuned without explanations, and OPT-RE, finetuned with explanations.
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good
Using state-of-the-art NLP models, we address each of these tasks and use them on the entire ACL Anthology, resulting in a visualization workspace that gives researchers a comprehensive overview of the field of NLP4SG.
Psychologically-Inspired Causal Prompts
In this work, we take sentiment classification as an example and look into the causal relations between the review (X) and sentiment (Y).
Natural Language Processing for Policymaking
This text is from Chapter 7 (pages 141-162) of the Handbook of Computational Social Science for Policy (2023).
Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion
Recent years have seen the rapid development of large generative models for text; however, much less research has explored the connection between text and another "language" of communication -- music.
Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing
Generated texts from large pretrained language models have been shown to exhibit a variety of harmful, human-like biases about various demographics.
Editing a Woman's Voice
However, when we trace those early drafts to their published versions, a substantial gender gap in linguistic uncertainty arises.
Differentially Private Language Models for Secure Data Sharing
To protect the privacy of individuals whose data is being shared, it is of high importance to develop methods allowing researchers and companies to release textual data while providing formal privacy guarantees to its originators.
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models
By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space.
State-of-the-art generalisation research in NLP: A taxonomy and review
We present a taxonomy for characterising and understanding generalisation research in NLP.
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Using a state-of-the-art large language model (LLM) as a basis, we propose a novel moral chain of thought (MORALCOT) prompting strategy that combines the strengths of LLMs with theories of moral reasoning developed in cognitive science to predict human moral judgments.
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance
We show that these two factors have a large causal effect on the MT performance, in addition to the test-model direction mismatch highlighted by existing work on the impact of translationese.
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang
We analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang words.
Logical Fallacy Detection
In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate).
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning
However, previous works rarely investigate the effects of a different number of classes (i. e., $N$-way) and number of labeled data per class (i. e., $K$-shot) during training vs. testing.
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research.
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain.
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation
With a human-annotated test set, we provide this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.
Ranked #1 on
Unsupervised KG-to-Text Generation
on GenWiki (Fine)
Deep Learning for Text Style Transfer: A Survey
Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others.
Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis
Based on the SemEval 2014 dataset, we construct the Aspect Robustness Test Set (ARTS) as a comprehensive probe of the aspect robustness of ABSA models.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG~2017 dataset after preprocessing, which is far fewer than the millions of data for other tasks such as machine translation.
Relation of the Relations: A New Paradigm of the Relation Extraction Problem
In natural language, often multiple entities appear in the same text.
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure.
A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation
State-of-the-art neural machine translation (NMT) systems are data-hungry and perform poorly on new domains with no supervised data.
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models.
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content.
GraphIE: A Graph-Based Framework for Information Extraction
Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies.
3D Traffic Simulation for Autonomous Vehicles in Unity and Python
Over the recent years, there has been an explosion of studies on autonomous vehicles.
