Papers by Wei-Fan Chen

6 papers
Explainable Hallucination through Natural Language Inference Mapping (2025.findings-acl)

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Challenge: Large language models (LLMs) often generate hallucinated content, making it crucial to identify and quantify inconsistencies in their outputs.
Approach: They propose a framework that maps entailment and contradiction relations between inputs and outputs using a natural language inference model.
Outcome: The proposed framework outperforms state-of-the-art methods by five percentage points while providing clear, interpretable explanations.
Detecting Media Bias in News Articles using Gaussian Bias Distributions (2020.findings-emnlp)

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Challenge: a new study shows that media bias is not only about honesty or accuracy, but also about taste or preference.
Approach: They propose to use second-order information to detect media bias in articles . they propose to analyze the frequency, positions, and sequential order of biased statements .
Outcome: The proposed model outperforms other models that use second-order information on biased statements on an existing media bias dataset.
Controlled Neural Sentence-Level Reframing of News Articles (2021.findings-emnlp)

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Challenge: a news article is framed from a specific perspective, but reframing can be difficult . a framed article can be used to communicate with opposing camps of audiences .
Approach: They propose to reframe news articles using a media frame corpus to achieve this . they propose three strategies to train neural models for reframing .
Outcome: The proposed techniques maintain coherence of sentences and reframe them correctly . the proposed techniques are effective but have tradeoffs .
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition (D19-50)

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Challenge: Existing methods to detect abusive language only train one classifier for the whole variety of offending . a new method can support a moderator with explicit unraveled explanations for why something was flagged as abusive .
Approach: a new method is proposed to distinguish explicitly abusive cases from the more "shadowed" ones . the researchers extend a lexicon of abusive terms to include new obfuscations of abusive words .
Outcome: a new method can distinguish explicitly abusive cases from the more "shadowed" ones . the method can support a moderator with explicit unraveled explanations for why something was flagged as abusive .
Reference-guided Style-Consistent Content Transfer (2024.lrec-main)

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Challenge: Text style transfer involves changing the style of a text while preserving its original style.
Approach: They propose a task of style-consistent content transfer which involves modifying a text’s content based on a provided reference statement while preserving its original style.
Outcome: The proposed approach meets three important conditions: reference faithfulness, style adherence, and coherence.
Belief-based Generation of Argumentative Claims (2021.eacl-main)

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Challenge: Existing methods to generate argument with the ability to encode beliefs are limited by the noise generated by the automatic collection of bag-of-words.
Approach: They propose to augment argument generation technology with ability to encode beliefs . they model users' beliefs via their stances on big issues and extend text generation models with extra input reflecting user's beliefs.
Outcome: The proposed approach is low in effectiveness because of the noise produced by the automatic collection of bag-of-words.

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