2.2k citations · 3k across the 44 of their papers we have counts for
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OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking
Chia-Hsuan Lee, Hao Cheng, Mari Ostendorf
Large language models (LLMs) have revolutionized the landscape of Natural Language Processing systems, but are computationally expensive. To reduce the cost without sacrificing per…
DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation
Yiqing Xie, Sheng Zhang, Hao Cheng +6
Medical text generation aims to assist with administrative work and highlight salient information to support decision-making. To reflect the specific requirements of medical text,…
Fast-ELECTRA for Efficient Pre-training
Chengyu Dong, Liyuan Liu, Hao Cheng +3
ELECTRA pre-trains language models by detecting tokens in a sequence that have been replaced by an auxiliary model. Although ELECTRA offers a significant boost in efficiency, its p…
ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks
Xiaodong Yu, Hao Cheng, Xiaodong Liu +2
Despite remarkable advancements in mitigating hallucinations in large language models (LLMs) by retrieval augmentation, it remains challenging to measure the reliability of LLMs us…
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
Bo-Ru Lu, Nikita Haduong, Chia-Hsuan Lee +7
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fac…
Augmenting Language Models with Long-Term Memory
Weizhi Wang, Li Dong, Hao Cheng +4
Existing large language models (LLMs) can only afford fix-sized inputs due to the input length limit, preventing them from utilizing rich long-context information from past inputs.…
Self-Verification Improves Few-Shot Clinical Information Extraction
Zelalem Gero, Chandan Singh, Hao Cheng +4
Extracting patient information from unstructured text is a critical task in health decision-support and clinical research. Large language models (LLMs) have shown the potential to…
Chain-of-Skills: A Configurable Model for Open-domain Question Answering
Kaixin Ma, Hao Cheng, Yu Zhang +3
The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated…
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding
Yu Zhang, Hao Cheng, Zhihong Shen +3
Scientific literature understanding tasks have gained significant attention due to their potential to accelerate scientific discovery. Pre-trained language models (LMs) have shown…
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
Pan Lu, Baolin Peng, Hao Cheng +5
Large language models (LLMs) have achieved remarkable progress in solving various natural language processing tasks due to emergent reasoning abilities. However, LLMs have inherent…
Pre-training Transformers for Knowledge Graph Completion
Sanxing Chen, Hao Cheng, Xiaodong Liu +3
Learning transferable representation of knowledge graphs (KGs) is challenging due to the heterogeneous, multi-relational nature of graph structures. Inspired by Transformer-based p…
Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
Baolin Peng, Michel Galley, Pengcheng He +8
Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. Howev…