5 citations · 7 across the 9 of their papers we have counts for
11 papers
Beyond Routing Weights: Faithful Response-Level Interpretation of Mixture-of-Experts Reward Models via Contribution Contrast
Yifan Wang, Jinyi Mu, Mayank Jobanputra +4
Reward models are central to learning from human preferences, yet identifying what drives their predictions remains challenging. Recent sparse Mixture-of-Experts (MoE) reward model…
Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling
Yifan Wang, Jinyi Mu, Mayank Jobanputra +5
Preference modeling plays a central role in reinforcement learning from human feedback (RLHF), enabling large language models (LLMs) to align with human values. However, most exist…
How AI Responses Shape User Beliefs: The Effects of Information Detail and Confidence on Belief Strength and Stance
Zekun Wu, Mayank Jobanputra, Vera Demberg +2
The growing use of AI-generated responses in everyday tools raises concern about how subtle features such as supporting detail or tone of confidence may shape people's beliefs. To…
Can LLMs subtract numbers?
Mayank Jobanputra, Nils Philipp Walter, Maitrey Mehta +7
We present a systematic study of subtraction in large language models (LLMs). While prior benchmarks emphasize addition and multiplication, subtraction has received comparatively l…
Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?
Yifan Wang, Mayank Jobanputra, Ji-Ung Lee +3
Natural language processing (NLP) models often replicate or amplify social bias from training data, raising concerns about fairness. At the same time, their black-box nature makes…
B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability
Yifan Wang, Sukrut Rao, Ji-Ung Lee +2
Post-hoc explanation methods for black-box models often struggle with faithfulness and human interpretability due to the lack of explainability in current neural architectures. Mea…
RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework
Yifan Wang, Vera Demberg
Despite significant advancements in natural language generation, controlling language models to produce texts with desired attributes remains a formidable challenge. In this work,…
RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization
Dongqi Liu, Vera Demberg
For long document summarization, discourse structure is important to discern the key content of the text and the differences in importance level between sentences. Unfortunately, t…
SciNews: From Scholarly Complexities to Public Narratives -- A Dataset for Scientific News Report Generation
Dongqi Liu, Yifan Wang, Jia Loy +1
Scientific news reports serve as a bridge, adeptly translating complex research articles into reports that resonate with the broader public. The automated generation of such narrat…
ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer
Dongqi Liu, Vera Demberg
Large-scale language models, like ChatGPT, have garnered significant media attention and stunned the public with their remarkable capacity for generating coherent text from short n…
Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization
Dongqi Liu, Yifan Wang, Vera Demberg
For text summarization, the role of discourse structure is pivotal in discerning the core content of a text. Regrettably, prior studies on incorporating Rhetorical Structure Theory…