52 citations · 55 across the 9 of their papers we have counts for
10 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…
Born a Transformer -- Always a Transformer? On the Effect of Pretraining on Architectural Abilities
Mayank Jobanputra, Yana Veitsman, Yash Sarrof +4
Transformers have theoretical limitations in modeling certain sequence-to-sequence tasks, yet it remains largely unclear if these limitations play a role in large-scale pretrained…
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…
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
Gowtham Ramesh, Sumanth Doddapaneni, Aravinth Bheemaraj +15
We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between Englis…
Detecting and analyzing collusive entities on YouTube
Hridoy Sankar Dutta, Mayank Jobanputra, Himani Negi +1
In this work, we provide an in-depth analysis of collusive entities on YouTube fostered by various blackmarket services. Following this, we propose models to detect three types of…
Unsupervised Question Answering for Fact-Checking
Mayank Jobanputra
Recent Deep Learning (DL) models have succeeded in achieving human-level accuracy on various natural language tasks such as question-answering, natural language inference (NLI), an…