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Found 18 papers, 8 papers with code
Aligning with Whom? Large Language Models Have Gender and Racial Biases in Subjective NLP Tasks
We find that for both tasks, model predictions are closer to the labels from White and female participants.
When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models
In this study, we present a systematic evaluation of personas in system prompts.
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research
Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks.
Exploring Linguistic Style Matching in Online Communities: The Role of Social Context and Conversation Dynamics
Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion.
When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Dataset
Further, our work shows that backgrounds not previously considered in NLP (e. g., education), are meaningful and should be considered.
Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark
Large language models (LLMs) have been shown to perform well at a variety of syntactic, discourse, and reasoning tasks.
POTATO: The Portable Text Annotation Tool
We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active learning, keypress shortcuts, keyword highlights, tooltips); and 3) supports a high degree of customization (editable UI, inserting pre-screening questions, attention and qualification tests).
A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale
Our analysis further reveal stereotypical portrayals of both male and female characters in terms of moral foundations and events.
Modeling Information Change in Science Communication with Semantically Matched Paraphrases
Whether the media faithfully communicate scientific information has long been a core issue to the science community.
SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
We propose MINT, a new Multilingual INTimacy analysis dataset covering 13, 372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic.
Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications
Here, we introduce a new study of certainty that models both the level and the aspects of certainty in scientific findings.
Quantifying Intimacy in Language
Intimacy is a fundamental aspect of how we relate to others in social settings.
MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization
Recently, large-scale datasets have vastly facilitated the development in nearly all domains of Natural Language Processing.
Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders
Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents.
SUM: Suboptimal Unitary Multi-task Learning Framework for Spatiotemporal Data Prediction
However, such a method have relatively weak performance when the task number is small, and we cannot integrate it into non-linear models.
Targeted Sentiment Analysis: A Data-Driven Categorization
Targeted sentiment analysis (TSA), also known as aspect based sentiment analysis (ABSA), aims at detecting fine-grained sentiment polarity towards targets in a given opinion document.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets
Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems.
S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information
In this paper, we propose Sequence to Sequence with Prototype Memory Network (S2SPMN) to exploit the relevant information provided by the large dialogue corpus to enhance response generation.
