19 citations · 27 across the 13 of their papers we have counts for
9 papers · 1 filter
Knowledge-Level Consistency Reinforcement Learning: Dual-Fact Alignment for Long-Form Factuality
Junliang Li, Yucheng Wang, Yan Chen +5
Hallucination in large language models (LLMs) during long-form generation remains difficult to address under existing reinforcement learning from human feedback (RLHF) frameworks,…
BEE-RAG: Balanced Entropy Engineering for Retrieval-Augmented Generation
Yuhao Wang, Ruiyang Ren, Yucheng Wang +4
With the rapid advancement of large language models (LLMs), retrieval-augmented generation (RAG) has emerged as a critical approach to supplement the inherent knowledge limitations…
Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation
Yuhao Wang, Ruiyang Ren, Yucheng Wang +4
Considering the inherent limitations of parametric knowledge in large language models (LLMs), retrieval-augmented generation (RAG) is widely employed to expand their knowledge scop…
Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation
Yuhao Wang, Ruiyang Ren, Yucheng Wang +4
Long-form question answering (LFQA) requires open-ended long-form responses that synthesize coherent, factually grounded content from multi-source evidence. This makes reinforcemen…
GLS-CSC: A Simple but Effective Strategy to Mitigate Chinese STM Models' Over-Reliance on Superficial Clue
Yanrui Du, Sendong Zhao, Yuhan Chen +5
Pre-trained models have achieved success in Chinese Short Text Matching (STM) tasks, but they often rely on superficial clues, leading to a lack of robust predictions. To address t…
Don't Ignore Dual Logic Ability of LLMs while Privatizing: A Data-Intensive Analysis in Medical Domain
Yanrui Du, Sendong Zhao, Muzhen Cai +4
Extensive studies have been devoted to privatizing general-domain Large Language Models (LLMs) as Domain-Specific LLMs via feeding specific-domain data. However, these privatizatio…
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
Ruiyang Ren, Yuhao Wang, Yingqi Qu +6
Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge. However, it remains unclear how well LLMs are able to perceive the…
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases
Yanchen Liu, Jing Yan, Yan Chen +2
Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness. T…
Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation
Yanrui Du, Jing Yan, Yan Chen +6
Recent research has revealed that deep neural networks often take dataset biases as a shortcut to make decisions rather than understand tasks, leading to failures in real-world app…