86 citations · 131 across the 55 of their papers we have counts for
8 papers · 1 filter
Towards Verifiable Text Generation with Evolving Memory and Self-Reflection
Hao Sun, Hengyi Cai, Bo Wang +5
Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also know…
Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
Weiwei Sun, Zheng Chen, Xinyu Ma +6
Recent studies have demonstrated the great potential of Large Language Models (LLMs) serving as zero-shot relevance rankers. The typical approach involves making comparisons betwee…
A Robust Semantics-based Watermark for Large Language Model against Paraphrasing
Jie Ren, Han Xu, Yiding Liu +4
Large language models (LLMs) have show great ability in various natural language tasks. However, there are concerns that LLMs are possible to be used improperly or even illegally.…
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment
Yukun Zhao, Lingyong Yan, Weiwei Sun +5
Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment…
MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
Pengyue Jia, Yiding Liu, Xiangyu Zhao +4
Query expansion, pivotal in search engines, enhances the representation of user information needs with additional terms. While existing methods expand queries using retrieved or ge…
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
Yukun Zhao, Lingyong Yan, Weiwei Sun +6
Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. However, recent literature reveals that LLMs generate nonfactual responses inter…
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
Qingqing Ge, Zeyuan Zhao, Yiding Liu +4
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train and prompt" has shown promising results in adapting GNNs to vario…
Exploring Memorization in Fine-tuned Language Models
Shenglai Zeng, Yaxin Li, Jie Ren +7
Large language models (LLMs) have shown great capabilities in various tasks but also exhibited memorization of training data, raising tremendous privacy and copyright concerns. Whi…