Search Results for author: Feiyu Xiong

Found 23 papers, 15 papers with code

xFinder: Robust and Pinpoint Answer Extraction for Large Language Models

1 code implementation20 May 2024 Qingchen Yu, Zifan Zheng, Shichao Song, Zhiyu Li, Feiyu Xiong, Bo Tang, Ding Chen

The continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their performance.

NewsBench: Systematic Evaluation of LLMs for Writing Proficiency and Safety Adherence in Chinese Journalistic Editorial Applications

no code implementations29 Feb 2024 Miao Li, Ming-Bin Chen, Bo Tang, Shengbin Hou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Peng Cheng, Yi Luo

This study presents NewsBench, a novel benchmark framework developed to evaluate the capability of Large Language Models (LLMs) in Chinese Journalistic Writing Proficiency (JWP) and their Safety Adherence (SA), addressing the gap between journalistic ethics and the risks associated with AI utilization.


Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs

1 code implementation17 Feb 2024 Xun Liang, Hanyu Wang, Shichao Song, Mengting Hu, Xunzhi Wang, Zhiyu Li, Feiyu Xiong, Bo Tang

In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG).

Attribute Language Modelling +2

Grimoire is All You Need for Enhancing Large Language Models

1 code implementation7 Jan 2024 Ding Chen, Shichao Song, Qingchen Yu, Zhiyu Li, Wenjin Wang, Feiyu Xiong, Bo Tang

In this paper, we propose a method SLEICL that involves learning from examples using strong language models and then summarizing and transferring these learned skills to weak language models for inference and application.

In-Context Learning

UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation

1 code implementation26 Nov 2023 Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Peng Cheng, Zhonghao Wang, Haiying Deng

These techniques encompass the use of directed hallucination induction and strategies that deliberately alter authentic text to produce hallucinations.

Benchmarking Hallucination +2

CodeKGC: Code Language Model for Generative Knowledge Graph Construction

2 code implementations18 Apr 2023 Zhen Bi, Jing Chen, Yinuo Jiang, Feiyu Xiong, Wei Guo, Huajun Chen, Ningyu Zhang

However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks.

Code Completion graph construction +1

BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion

no code implementations16 Jul 2022 Fanglin Chen, Xiao Liu, Bo Tang, Feiyu Xiong, Serim Hwang, Guomian Zhuang

During deployment, we combine the offline RL model with the LP model to generate a robust policy under the budget constraints.

Offline RL reinforcement-learning +1

Disentangled Ontology Embedding for Zero-shot Learning

1 code implementation8 Jun 2022 Yuxia Geng, Jiaoyan Chen, Wen Zhang, Yajing Xu, Zhuo Chen, Jeff Z. Pan, Yufeng Huang, Feiyu Xiong, Huajun Chen

In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.

Image Classification Ontology Embedding +2

Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction

no code implementations12 May 2022 Tianshu Wang, Hongyu Lin, Cheng Fu, Xianpei Han, Le Sun, Feiyu Xiong, Hui Chen, Minlong Lu, Xiuwen Zhu

Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.

Entity Resolution

Ontology-enhanced Prompt-tuning for Few-shot Learning

no code implementations27 Jan 2022 Hongbin Ye, Ningyu Zhang, Shumin Deng, Xiang Chen, Hui Chen, Feiyu Xiong, Xi Chen, Huajun Chen

Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text.

Event Extraction Few-Shot Learning +1

SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning

1 code implementation17 Jan 2022 Yushi Bai, Xin Lv, Juanzi Li, Lei Hou, Yincen Qu, Zelin Dai, Feiyu Xiong

Multi-hop knowledge graph (KG) reasoning has been widely studied in recent years to provide interpretable predictions on missing links with evidential paths.

Decoder Navigate +1

Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules

no code implementations16 Dec 2021 Wen Zhang, Shumin Deng, Mingyang Chen, Liang Wang, Qiang Chen, Feiyu Xiong, Xiangwen Liu, Huajun Chen

We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems.

Entity Embeddings Graph Attention +4

Interpretable performance analysis towards offline reinforcement learning: A dataset perspective

no code implementations12 May 2021 Chenyang Xi, Bo Tang, Jiajun Shen, Xinfu Liu, Feiyu Xiong, Xueying Li

We make it open-source for fair and comprehensive competitions between offline RL algorithms with complete datasets and checkpoints being provided.

Offline RL Q-Learning +2

Studying Product Competition Using Representation Learning

no code implementations21 May 2020 Fanglin Chen, Xiao Liu, Davide Proserpio, Isamar Troncoso, Feiyu Xiong

We show that, compared with state-of-the-art models, our approach is faster, and can produce more accurate demand forecasts and price elasticities.

Causal Inference Decision Making +1

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