13 citations · 40 across the 29 of their papers we have counts for
21 papers · 1 filter
BioVerge: A Comprehensive Benchmark and Study of Self-Evaluating Agents for Biomedical Hypothesis Generation
Fuyi Yang, Chenchen Ye, Mingyu Derek Ma +3
Hypothesis generation in biomedical research has traditionally centered on uncovering hidden relationships within vast scientific literature, often using methods like Literature-Ba…
Entropy-Based Adaptive Weighting for Self-Training
Xiaoxuan Wang, Yihe Deng, Mingyu Derek Ma +1
The mathematical problem-solving capabilities of large language models have become a focal point of research, with growing interests in leveraging self-generated reasoning paths as…
Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection
Mingyu Derek Ma, Yanna Ding, Zijie Huang +3
Generative Language Models rely on autoregressive decoding to produce the output sequence token by token. Many tasks such as preference optimization, require the model to produce t…
Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction
Mingyu Derek Ma, Xiaoxuan Wang, Yijia Xiao +4
Clinical diagnosis prediction models, when provided with a patient's medical history, aim to detect potential diseases early, facilitating timely intervention and improving prognos…
CLIMB: A Benchmark of Clinical Bias in Large Language Models
Yubo Zhang, Shudi Hou, Mingyu Derek Ma +3
Large language models (LLMs) are increasingly applied to clinical decision-making. However, their potential to exhibit bias poses significant risks to clinical equity. Currently, t…
MIRAI: Evaluating LLM Agents for Event Forecasting
Chenchen Ye, Ziniu Hu, Yihe Deng +4
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.…
CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Making
Mingyu Derek Ma, Chenchen Ye, Yu Yan +4
The integration of Artificial Intelligence (AI), especially Large Language Models (LLMs), into the clinical diagnosis process offers significant potential to improve the efficiency…
Improving Event Definition Following For Zero-Shot Event Detection
Zefan Cai, Po-Nien Kung, Ashima Suvarna +6
Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches…
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach
Yanchen Liu, Mingyu Derek Ma, Wenna Qin +5
Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals' mental processes that is not observable. Existing susceptibi…
Mitigating Bias for Question Answering Models by Tracking Bias Influence
Mingyu Derek Ma, Jiun-Yu Kao, Arpit Gupta +6
Models of various NLP tasks have been shown to exhibit stereotypes, and the bias in the question answering (QA) models is especially harmful as the output answers might be directly…
STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models
Mingyu Derek Ma, Xiaoxuan Wang, Po-Nien Kung +3
Information extraction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies. They heavily rely on task-specific traini…
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models
Jiashu Xu, Mingyu Derek Ma, Fei Wang +2
We investigate security concerns of the emergent instruction tuning paradigm, that models are trained on crowdsourced datasets with task instructions to achieve superior performanc…
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
Mingyu Derek Ma, Jiun-Yu Kao, Shuyang Gao +4
Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs. Existing works fine-tune all language model (LM) parameters to tackle the…
Multi-hop Evidence Retrieval for Cross-document Relation Extraction
Keming Lu, I-Hung Hsu, Wenxuan Zhou +2
Relation Extraction (RE) has been extended to cross-document scenarios because many relations are not simply described in a single document. This inevitably brings the challenge of…
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?
Jiashu Xu, Mingyu Derek Ma, Muhao Chen
Two key obstacles in biomedical relation extraction (RE) are the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels due to low annotation…
DICE: Data-Efficient Clinical Event Extraction with Generative Models
Mingyu Derek Ma, Alexander K. Taylor, Wei Wang +1
Event extraction for the clinical domain is an under-explored research area. The lack of training data along with the high volume of domain-specific terminologies with vague entity…
Summarization as Indirect Supervision for Relation Extraction
Keming Lu, I-Hung Hsu, Wenxuan Zhou +2
Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise ex…
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
Mingyu Derek Ma, Muhao Chen, Te-Lin Wu +1
Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works atte…
EventPlus: A Temporal Event Understanding Pipeline
Mingyu Derek Ma, Jiao Sun, Mu Yang +5
We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, ev…
Implicit Discourse Relation Identification for Open-domain Dialogues
Mingyu Derek Ma, Kevin K. Bowden, Jiaqi Wu +2
Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especial…
Dual Memory Network Model for Biased Product Review Classification
Yunfei Long, Mingyu Ma, Qin Lu +2
In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified mod…