Search Results for author:
Found 26 papers, 8 papers with code
Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate
In this paper, we propose a novel problem formulation to mine arguments from Twitter: We formulate argument mining on Twitter as a text classification task to identify tweets that serve as premises for a hashtag that represents a claim of interest.
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning
Automatic fact-checking is crucial for recognizing misinformation spreading on the internet.
Return of EM: Entity-driven Answer Set Expansion for QA Evaluation
Recently, directly using large language models (LLMs) has been shown to be the most reliable method to evaluate QA models.
AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence
Therefore, we've completed a benchmark encompassing daily life questions, diverse corresponding responses, and majority vote ranking to train our helpfulness metric.
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.
Argument Quality Assessment in the Age of Instruction-Following Large Language Models
The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like.
A Design Space for Intelligent and Interactive Writing Assistants
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities.
LifeTox: Unveiling Implicit Toxicity in Life Advice
As large language models become increasingly integrated into daily life, detecting implicit toxicity across diverse contexts is crucial.
On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model
An exciting advancement in the field of multilingual models is the emergence of autoregressive models with zero- and few-shot capabilities, a phenomenon widely reported in large-scale language models.
From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models
Being able to predict people's opinions on issues and behaviors in realistic scenarios can be helpful in various domains, such as politics and marketing.
Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ -- via few-shot prompting leveraging external knowledge -- and uses it to generate a long-form answer.
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration
The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising.
KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application
Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data.
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
We first present CAMBIGNQ, a dataset consisting of 5, 654 ambiguous questions, each with relevant passages, possible answers, and a clarification question.
Critic-Guided Decoding for Controlled Text Generation
In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding.
ClaimDiff: Comparing and Contrasting Claims on Contentious Issues
With the growing importance of detecting misinformation, many studies have focused on verifying factual claims by retrieving evidence.
Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking
To this end, the latest approach is to train a factual consistency classifier on factually consistent and inconsistent summaries.
Plug-and-Play Adaptation for Continuously-updated QA
To this end, we first propose a novel task--Continuously-updated QA (CuQA)--in which multiple large-scale updates are made to LMs, and the performance is measured with respect to the success in adding and updating knowledge while retaining existing knowledge.
Automated Fact-Checking of Claims from Wikipedia
However, such datasets suffer from limited applicability due to the synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet.
Argument Mining with Structured SVMs and RNNs
We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure.
A Corpus of Argument Networks: Using Graph Properties to Analyse Divisive Issues
Governments are increasingly utilising online platforms in order to engage with, and ascertain the opinions of, their citizens.
