5 citations · 11 across the 5 of their papers we have counts for
6 papers
Towards Understanding What State Space Models Learn About Code
Jiali Wu, Abhinav Anand, Shweta Verma +1
State Space Models (SSMs) have emerged as an efficient alternative to the Transformer architecture. Prior work shows that, when trained under comparable conditions, SSMs can match…
From Parameter to Representation: A Closed-Form Approach for Controllable Model Merging
Jialin Wu, Jian Yang, Handing Wang +2
Model merging combines expert models for multitask performance but faces challenges from parameter interference. This has sparked recent interest in controllable model merging, giv…
EnchTable: Unified Safety Alignment Transfer in Fine-tuned Large Language Models
Jialin Wu, Kecen Li, Zhicong Huang +3
Many machine learning models are fine-tuned from large language models (LLMs) to achieve high performance in specialized domains like code generation, biomedical analysis, and math…
Benchmark on Drug Target Interaction Modeling from a Drug Structure Perspective
Xinnan Zhang, Jialin Wu, Junyi Xie +2
The prediction modeling of drug-target interactions is crucial to drug discovery and design, which has seen rapid advancements owing to deep learning technologies. Recently develop…
GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning
Mehran Kazemi, Hamidreza Alvari, Ankit Anand +3
Large language models have shown impressive results for multi-hop mathematical reasoning when the input question is only textual. Many mathematical reasoning problems, however, con…
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling
Yaqing Wang, Jialin Wu, Tanmaya Dabral +8
Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on a wide range of tasks by scaling up parameter counts from O(10^9) to O(10^{12})…