84 citations · 410 across the 35 of their papers we have counts for
39 papers
Upstream Influence, Downstream Re-Anchoring in LLM Agent Chains
Chun-Yu Hsu, Lun-Wei Ku, Chung-Chi Chen
People act on evaluative judgments produced by LLM agents, and in multi-agent systems those judgments pass through several agents before reaching a person. We show that a workflow'…
SIGN-MAPPO: State-Intent-Aware Graph Multi-Agent Reinforcement Learning for Cooperative Driving under Imperfect V2X
Zide Fan, Jiahui Yuan, Kaixin Zhang +3
Cooperative driving at mixed-traffic unsignalized intersections is challenged by imperfect vehicle-to-everything (V2X) communication, human-driven vehicle (HV) intentions that cann…
Walk-on-Ambient-Patch: Solver-Adapted Isometric Embeddings for Projected Walk on Spheres
Yunfeng Liao, Xiucheng Li
Projected Walk on Spheres (PWoS) solves surface PDEs through ambient sampling and closest-point projection, but tightly folded embeddings can force short steps or ambiguous project…
Positional Cognitive Specialization: Where Do LLMs Learn To Comprehend and Speak Your Language?
Luis Frentzen Salim, Lun-Wei Ku, Hsing-Kuo Kenneth Pao
Adapting large language models (LLMs) to new languages is an expensive and opaque process. Understanding how language models acquire new languages and multilingual abilities is key…
Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation
Luis Frentzen Salim, Esteban Carlin, Alexandre Morinvil +2
Building machine translation (MT) systems for low-resource languages is notably difficult due to the scarcity of high-quality data. Although Large Language Models (LLMs) have impro…
Profile-LLM: Dynamic Profile Optimization for Realistic Personality Expression in LLMs
Shi-Wei Dai, Yan-Wei Shie, Tsung-Huan Yang +2
Personalized Large Language Models (LLMs) have been shown to be an effective way to create more engaging and enjoyable user-AI interactions. While previous studies have explored us…
Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023
Ting-Yao E. Hsu, Yi-Li Hsu, Shaurya Rohatgi +8
Since the SciCap datasets launch in 2021, the research community has made significant progress in generating captions for scientific figures in scholarly articles. In 2023, the fir…
SocialNLP Fake-EmoReact 2021 Challenge Overview: Predicting Fake Tweets from Their Replies and GIFs
Chien-Kun Huang, Yi-Ting Chang, Lun-Wei Ku +2
This paper provides an overview of the Fake-EmoReact 2021 Challenge, held at the 9th SocialNLP Workshop, in conjunction with NAACL 2021. The challenge requires predicting the authe…
Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term
Yi-Li Hsu, Shih-Chieh Dai, Aiping Xiong +1
With advancements in natural language processing (NLP) models, automatic explanation generation has been proposed to mitigate misinformation on social media platforms in addition t…
Location-Aware Visual Question Generation with Lightweight Models
Nicholas Collin Suwono, Justin Chih-Yao Chen, Tun Min Hung +5
This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical lo…
LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis
Shih-Chieh Dai, Aiping Xiong, Lun-Wei Ku
Thematic analysis (TA) has been widely used for analyzing qualitative data in many disciplines and fields. To ensure reliable analysis, the same piece of data is typically assigned…
HonestBait: Forward References for Attractive but Faithful Headline Generation
Chih-Yao Chen, Dennis Wu, Lun-Wei Ku
Current methods for generating attractive headlines often learn directly from data, which bases attractiveness on the number of user clicks and views. Although clicks or views do r…
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification
Chih-Yao Chen, Tun-Min Hung, Yi-Li Hsu +1
Fine-grained emotion classification (FEC) is a challenging task. Specifically, FEC needs to handle subtle nuance between labels, which can be complex and confusing. Most existing m…
Multi-VQG: Generating Engaging Questions for Multiple Images
Min-Hsuan Yeh, Vicent Chen, Ting-Hao 'Kenneth' Haung +1
Generating engaging content has drawn much recent attention in the NLP community. Asking questions is a natural way to respond to photos and promote awareness. However, most answer…
Ask to Know More: Generating Counterfactual Explanations for Fake Claims
Shih-Chieh Dai, Yi-Li Hsu, Aiping Xiong +1
Automated fact checking systems have been proposed that quickly provide veracity prediction at scale to mitigate the negative influence of fake news on people and on public opinion…
Let's Talk! Striking Up Conversations via Conversational Visual Question Generation
Shih-Han Chan, Tsai-Lun Yang, Yun-Wei Chu +4
An engaging and provocative question can open up a great conversation. In this work, we explore a novel scenario: a conversation agent views a set of the user's photos (for example…
Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction
Chang-You Tai, Ming-Yao Li, Lun-Wei Ku
Automatic identification of salient aspects from user reviews is especially useful for opinion analysis. There has been significant progress in utilizing weakly supervised approach…
Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter
Boaz Shmueli, Soumya Ray, Lun-Wei Ku
Datasets with induced emotion labels are scarce but of utmost importance for many NLP tasks. We present a new, automated method for collecting texts along with their induced reacti…
Plot and Rework: Modeling Storylines for Visual Storytelling
Chi-Yang Hsu, Yun-Wei Chu, Ting-Hao 'Kenneth' Huang +1
Writing a coherent and engaging story is not easy. Creative writers use their knowledge and worldview to put disjointed elements together to form a coherent storyline, and work and…
Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing
Boaz Shmueli, Jan Fell, Soumya Ray +1
The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding…
SocialNLP EmotionGIF 2020 Challenge Overview: Predicting Reaction GIF Categories on Social Media
Boaz Shmueli, Lun-Wei Ku, Soumya Ray
We present an overview of the EmotionGIF2020 Challenge, held at the 8th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with ACL…
Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent
Yun-Hsuan Jen, Chieh-Yang Huang, Mei-Hua Chen +2
Many English-as-a-second language learners have trouble using near-synonym words (e.g., small vs.little; briefly vs.shortly) correctly, and often look for example sentences to lear…
Reactive Supervision: A New Method for Collecting Sarcasm Data
Boaz Shmueli, Lun-Wei Ku, Soumya Ray
Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utili…
MVIN: Learning Multiview Items for Recommendation
Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu +2
Researchers have begun to utilize heterogeneous knowledge graphs (KGs) as auxiliary information in recommendation systems to mitigate the cold start and sparsity issues. However, u…