2 citations · 6 across the 16 of their papers we have counts for
17 papers
When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
Yuqing Yang, Qi Zhu, Zhen Han +5
While large language models (LLMs) perform well on table tasks, they still make data referencing errors (DREs), i.e., incorrectly citing or omitting table values, despite understan…
ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
Zelin He, Haotian Lin, Boran Han +6
Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strate…
LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents
Haoyang Fang, Wei Zhu, Boran Han +11
RL post-training strategies are dataset-dependent and reveal a recurring empirical pattern: capacity parameters accumulate monotonically across stages, while regularization paramet…
Efficient Table Retrieval and Understanding with Multimodal Large Language Models
Zhuoyan Xu, Haoyang Fang, Boran Han +4
Tabular data is frequently captured in image form across a wide range of real-world scenarios such as financial reports, handwritten records, and document scans. These visual repre…
HetRL: Efficient Reinforcement Learning for LLMs in Heterogeneous Environments
Yongjun He, Shuai Zhang, Jiading Gai +5
As large language models (LLMs) continue to scale and new GPUs are released even more frequently, there is an increasing demand for LLM post-training in heterogeneous environments…
CrEst: Credibility Estimation for Contexts in LLMs via Weak Supervision
Dyah Adila, Shuai Zhang, Boran Han +2
The integration of contextual information has significantly enhanced the performance of large language models (LLMs) on knowledge-intensive tasks. However, existing methods often o…
Visual Instruction Tuning with Chain of Region-of-Interest
Yixin Chen, Shuai Zhang, Boran Han +1
High-resolution (HR) images are pivotal for enhancing the recognition and understanding capabilities of multimodal large language models (MLLMs). However, directly increasing image…
Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
Dheeraj Baby, Boran Han, Shuai Zhang +3
We study the well-motivated problem of online distribution shift in which the data arrive in batches and the distribution of each batch can change arbitrarily over time. Since the…
Effectively Steer LLM To Follow Preference via Building Confident Directions
Bingqing Song, Boran Han, Shuai Zhang +5
Having an LLM that aligns with human preferences is essential for accommodating individual needs, such as maintaining writing style or generating specific topics of interest. The m…
Unraveling the Gradient Descent Dynamics of Transformers
Bingqing Song, Boran Han, Shuai Zhang +2
While the Transformer architecture has achieved remarkable success across various domains, a thorough theoretical foundation explaining its optimization dynamics is yet to be fully…
Transferring Knowledge from Large Foundation Models to Small Downstream Models
Shikai Qiu, Boran Han, Danielle C. Maddix +3
How do we transfer the relevant knowledge from ever larger foundation models into small, task-specific downstream models that can run at much lower costs? Standard transfer learnin…
Discovering Bias in Latent Space: An Unsupervised Debiasing Approach
Dyah Adila, Shuai Zhang, Boran Han +1
The question-answering (QA) capabilities of foundation models are highly sensitive to prompt variations, rendering their performance susceptible to superficial, non-meaning-alterin…
CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving
Pei Chen, Boran Han, Shuai Zhang
Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. However, t…
Bridging Remote Sensors with Multisensor Geospatial Foundation Models
Boran Han, Shuai Zhang, Xingjian Shi +1
In the realm of geospatial analysis, the diversity of remote sensors, encompassing both optical and microwave technologies, offers a wealth of distinct observational capabilities.…
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design
Wenqi Jiang, Shuai Zhang, Boran Han +3
Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases. However, retrievals from large da…
CaMML: Context-Aware Multimodal Learner for Large Models
Yixin Chen, Shuai Zhang, Boran Han +2
In this work, we introduce Context-Aware MultiModal Learner (CaMML), for tuning large multimodal models (LMMs). CaMML, a lightweight module, is crafted to seamlessly integrate mult…
Lightweight In-Context Tuning for Multimodal Unified Models
Yixin Chen, Shuai Zhang, Boran Han +1
In-context learning (ICL) involves reasoning from given contextual examples. As more modalities comes, this procedure is becoming more challenging as the interleaved input modaliti…