14 citations · 18 across the 14 of their papers we have counts for
14 papers
BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization
Saket Reddy, Ke Yang, ChengXiang Zhai
Mitigating social bias in Large Language Models (LLMs) presents a distinct alignment challenge: unlike verifiable tasks, bias lacks a single ground truth, creating a high-variance,…
Benchmarking Multi-turn Medical Diagnosis: Hold, Lure, and Self-Correction
Jinrui Fang, Runhan Chen, Xu Yang +9
Large language models (LLMs) achieve high accuracy in medical diagnosis when all clinical information is provided in a single turn, yet how they behave under multi-turn evidence ac…
Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation
Chenkai Sun, Denghui Zhang, ChengXiang Zhai +1
Given the growing influence of language model-based agents on high-stakes societal decisions, from public policy to healthcare, ensuring their beneficial impact requires understand…
Atomic Reasoning for Scientific Table Claim Verification
Yuji Zhang, Qingyun Wang, Cheng Qian +7
Scientific texts often convey authority due to their technical language and complex data. However, this complexity can sometimes lead to the spread of misinformation. Non-experts a…
ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges
Cheng Qian, Hongyi Du, Hongru Wang +6
Recent progress in large language models (LLMs) has enabled substantial advances in solving mathematical problems. However, existing benchmarks often fail to reflect the complexity…
Leveraging LLMs for Predicting Unknown Diagnoses from Clinical Notes
Dina Albassam, Adam Cross, Chengxiang Zhai
Electronic Health Records (EHRs) often lack explicit links between medications and diagnoses, making clinical decision-making and research more difficult. Even when links exist, di…
The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination
Yuji Zhang, Sha Li, Cheng Qian +8
Hallucination is a persistent challenge in large language models (LLMs), where even with rigorous quality control, models often generate distorted facts. This paradox, in which err…
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…
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov +2
The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…
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…
Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations
Revanth Gangi Reddy, Hao Bai, Wentao Yao +3
Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations. However, it can be challenging to determine what in…
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting
Chenkai Sun, Jinning Li, Yi R. Fung +4
Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative…