Search Results for author:
Found 27 papers, 10 papers with code
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues.
Mitigating Framing Bias with Polarity Minimization Loss
Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events.
Towards Mitigating Hallucination in Large Language Models via Self-Reflection
Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks.
Survey of Social Bias in Vision-Language Models
This survey aims to provide researchers with a high-level insight into the similarities and differences of social bias studies in pre-trained models across NLP, CV, and VL.
Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis
To address this, we introduce CREHate, a CRoss-cultural English Hate speech dataset.
KoBBQ: Korean Bias Benchmark for Question Answering
In this paper, we present KoBBQ, a Korean bias benchmark dataset, and we propose a general framework that addresses considerations for cultural adaptation of a dataset.
Machine Learning-Based Multi-Objective Design Exploration Of Flexible Disc Elements
To accomplish this objective, we employ ANN coupled with genetic algorithm in the design exploration step to identify designs that meet the specified criteria (torque and misalignment) while having minimum mass and stress.
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
It is, for example, better at deductive than inductive reasoning.
RHO ($ρ$): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses.
Evaluating Parameter Efficient Learning for Generation
For cross-domain and cross-dataset cases, we show that (a) Adapter (Houlsby et al., 2019) performs the best amongst all the PERMs studied here, and (b) it outperforms finetuning if the task dataset is below a certain size.
Factuality Enhanced Language Models for Open-Ended Text Generation
In this work, we measure and improve the factual accuracy of large-scale LMs for open-ended text generation.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Towards Answering Open-ended Ethical Quandary Questions
We explore the current capability of LLMs in providing an answer with a deliberative exchange of different perspectives to an ethical quandary, in the approach of Socratic philosophy, instead of providing a closed answer like an oracle.
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias
Based on our discovery that title provides a good signal for framing bias, we present NeuS-TITLE that learns to neutralize news content in hierarchical order from title to article.
Survey of Hallucination in Natural Language Generation
This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation.
Assessing Political Prudence of Open-domain Chatbots
In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence.
Dynamically Addressing Unseen Rumor via Continual Learning
Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model.
On Unifying Misinformation Detection
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup.
Mitigating Media Bias through Neutral Article Generation
Media bias can lead to increased political polarization, and thus, the need for automatic mitigation methods is growing.
Towards Few-Shot Fact-Checking via Perplexity
Through experiments, we empirically verify the plausibility of the rather surprising usage of the perplexity score in the context of fact-checking and highlight the strength of our few-shot methodology by comparing it to strong fine-tuning-based baseline models.
Misinformation Has High Perplexity
Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly.
Language Models as Fact Checkers?
Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data.
Understanding the Shades of Sexism in Popular TV Series
[Multiple-submission] In the midst of a generation widely exposed to and influenced by media entertainment, the NLP research community has shown relatively little attention on the sexist comments in popular TV series.
Exploring Social Bias in Chatbots using Stereotype Knowledge
Exploring social bias in chatbot is an important, yet relatively unexplored problem.
Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data
This paper describes our system that has been submitted to SemEval-2019 Task 4: Hyperpartisan News Detection.
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases.
