Fei Huang
19 papers in the PaperMetrix corpus
Papers by this author
-
Research on Application of Blockchain Technology in Cloud-Network Collaboration
2020
With the deep integration of blockchain, cloud computing and 5G technology, the business model of the Internet is also undergoing subversive changes. Based on the current development trends of the industry, this article illustrates the …
-
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks
2022 · arXiv (Cornell University)
Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks). Existing augmentation techniques either manipulate the words in the original text …
-
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
2022 · arXiv (Cornell University)
We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG …
-
Knowledge Rumination for Pre-trained Language Models
2023 · arXiv (Cornell University)
Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted to integrate external knowledge into PLMs. However, despite the promising outcome, …
-
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality
2023
Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
-
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection
2023
Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning. Recent multi-modal OOD detection leverages textual information from in-distribution (ID) class names for visual OOD detection, yet it currently neglects the rich contextual information …
-
Extend Model Merging from Fine-Tuned to Pre-Trained Large Language Models via Weight Disentanglement
2024 · arXiv (Cornell University)
Merging Large Language Models (LLMs) aims to amalgamate multiple homologous LLMs into one with all the capabilities. Ideally, any LLMs sharing the same backbone should be mergeable, irrespective of whether they are Fine-Tuned (FT) with …
-
On the Role of Attention Heads in Large Language Model Safety
2024 · arXiv (Cornell University)
Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that …
-
Knowledge Mechanisms in Large Language Models: A Survey and Perspective
2024
Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang. Findings of the Association for Computational Linguistics: EMNLP 2024. …
-
ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration
2024 · arXiv (Cornell University)
Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhaustively retrain LLMs with new API knowledge. This limitation hampers …
-
SWE-GPT: A Process-Centric Language Model for Automated Software Improvement
2025 · Proceedings of the ACM on software engineering.
Large language models (LLMs) have demonstrated remarkable performance in code generation, significantly enhancing the coding efficiency of developers. Recent advancements in LLM-based agents have led to significant progress in end-to-end automatic software engineering (ASE), particularly …
-
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement
2025
Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume …
-
Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics
2025 · arXiv (Cornell University)
RAG (Retrieval-Augmented Generation) systems and web agents are increasingly evaluated on multi-hop deep search tasks, yet current practice suffers from two major limitations. First, most benchmarks leak the reasoning path in the question text, allowing …
-
Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute
2025 · arXiv (Cornell University)
Recent advancements in software engineering agents have demonstrated promising capabilities in automating program improvements. However, their reliance on closed-source or resource-intensive models introduces significant deployment challenges in private environments, prompting a critical question: \textit{How can …
-
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
2020 · Transactions of the Association for Computational Linguistics
Story generation, namely, generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural language generation models (e.g., GPT-2) …
-
A Joint Neural Model for Information Extraction with Global Features
2020
Most existing joint neural models for Information Extraction (IE) use local task-specific classifiers to predict labels for individual instances (e.g., trigger, relation) regardless of their interactions. For example, a VICTIM of a DIE event is …
-
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
2021
Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language …
-
Document-level Relation Extraction as Semantic Segmentation
2021
Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the …
-
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
2022 · Proceedings of the ACM Web Conference 2022
Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language …