200 citations · 784 across the 50 of their papers we have counts for
15 papers · 1 filter
Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
Aoran Gan, Hao Yu, Kai Zhang +5
Recent advancements in Retrieval-Augmented Generation (RAG) have revolutionized natural language processing by integrating Large Language Models (LLMs) with external information re…
The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation
Zheng Zhang, Ning Li, Qi Liu +6
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant document from external knowledge sources. By referencing this external knowledge,…
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models
Liyang He, Chenglong Liu, Rui Li +4
Sentence embedding is essential for many NLP tasks, with contrastive learning methods achieving strong performance using annotated datasets like NLI. Yet, the reliance on manual la…
What Makes In-context Learning Effective for Mathematical Reasoning: A Theoretical Analysis
Jiayu Liu, Zhenya Huang, Chaokun Wang +3
Owing to the capability of in-context learning, large language models (LLMs) have shown impressive performance across diverse mathematical reasoning benchmarks. However, we find th…
End-to-End Graph Flattening Method for Large Language Models
Bin Hong, Jinze Wu, Jiayu Liu +5
In recent years, the breakthrough of Large Language Models (LLMs) offers new ideas for achieving universal methods on graph data. The common practice of converting graphs into natu…
EduNLP: Towards a Unified and Modularized Library for Educational Resources
Zhenya Huang, Yuting Ning, Longhu Qin +8
Educational resource understanding is vital to online learning platforms, which have demonstrated growing applications recently. However, researchers and developers always struggle…
Can LLMs Solve longer Math Word Problems Better?
Xin Xu, Tong Xiao, Zitong Chao +3
Math Word Problems (MWPs) play a vital role in assessing the capabilities of Large Language Models (LLMs), yet current research primarily focuses on questions with concise contexts…
Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
Mingyue Cheng, Hao Zhang, Jiqian Yang +7
Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating large language models have been proposed in thi…
A Knowledge-Injected Curriculum Pretraining Framework for Question Answering
Xin Lin, Tianhuang Su, Zhenya Huang +3
Knowledge-based question answering (KBQA) is a key task in NLP research, and also an approach to access the web data and knowledge, which requires exploiting knowledge graphs (KGs)…
Position: AI Evaluation Should Learn from How We Test Humans
Yan Zhuang, Qi Liu, Zachary A. Pardos +5
As AI systems continue to evolve, their rigorous evaluation becomes crucial for their development and deployment. Researchers have constructed various large-scale benchmarks to det…
A Novel Approach for Auto-Formulation of Optimization Problems
Yuting Ning, Jiayu Liu, Longhu Qin +6
In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of…
Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training
Yuting Ning, Zhenya Huang, Xin Lin +4
Understanding mathematical questions effectively is a crucial task, which can benefit many applications, such as difficulty estimation. Researchers have drawn much attention to des…
Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification
Kai Zhang, Qi Liu, Zhenya Huang +5
Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domai…
Learning to Order Sub-questions for Complex Question Answering
Yunan Zhang, Xiang Cheng, Yufeng Zhang +5
Answering complex questions involving multiple entities and relations is a challenging task. Logically, the answer to a complex question should be derived by decomposing the comple…
Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems
Binbin Jin, Enhong Chen, Hongke Zhao +4
In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers. Existing solut…