14 citations · 18 across the 26 of their papers we have counts for
12 papers · 1 filter
MedProb: Probing Internal Representations of Vision-Language Models for Medical Question Answering
Erfan Nourbakhsh, Ke Yang, Anthony Rios
Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit this assumption with \textbf…
StudentSim: Training LLM-based Student Simulators
Ke Yang, Chenglong Wang, Michel Galley +4
AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for which student is sparse, slow…
TRACER: Early Failure Detection for Task-Oriented Dialogue
Erfan Nourbakhsh, Rocky Slavin, Ke Yang +1
Task-oriented dialogue systems often fail before the final breakdown is obvious, but most evaluation only measures failure after the conversation has already gone wrong. We present…
PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
Ke Yang, Zixi Chen, Xuan He +6
Long-term memory is essential for large language model (LLM) agents operating in complex environments, yet existing memory designs are either task-specific and non-transferable, or…
CRMWeaver: Building Powerful Business Agent via Agentic RL and Shared Memories
Yilong Lai, Yipin Yang, Ting Liang +4
Recent years have witnessed the rapid development of LLM-based agents, which shed light on using language agents to solve complex real-world problems. A prominent application lies…
A Multi-Agent Framework for Mitigating Dialect Biases in Privacy Policy Question-Answering Systems
Đorđe Klisura, Astrid R Bernaga Torres, Anna Karen Gárate-Escamilla +4
Privacy policies inform users about data collection and usage, yet their complexity limits accessibility for diverse populations. Existing Privacy Policy Question Answering (QA) sy…
TinyHelen's First Curriculum: Training and Evaluating Tiny Language Models in a Simpler Language Environment
Ke Yang, Volodymyr Kindratenko, ChengXiang Zhai
Training language models (LMs) and their application agents is increasingly costly due to large datasets and models, making test failures difficult to bear. Simplified language env…
ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study
Eric Modesitt, Ke Yang, Spencer Hulsey +2
Recent advances in language modeling demonstrate the need for high-quality domain-specific training data, especially for tasks that require specialized knowledge. General-purpose m…
Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency
Yiran Liu, Ke Yang, Zehan Qi +3
We present a novel statistical framework for analyzing stereotypes in large language models (LLMs) by systematically estimating the bias and variation in their generation. Current…
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
Chenkai Sun, Ke Yang, Revanth Gangi Reddy +5
The increasing demand for personalized interactions with large language models (LLMs) calls for methodologies capable of accurately and efficiently identifying user opinions and pr…
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Ke Yang, Jiateng Liu, John Wu +9
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural langua…
ADEPT: A DEbiasing PrompT Framework
Ke Yang, Charles Yu, Yi Fung +2
Several works have proven that finetuning is an applicable approach for debiasing contextualized word embeddings. Similarly, discrete prompts with semantic meanings have shown to b…