181 citations · 352 across the 50 of their papers we have counts for
11 papers · 1 filter
Can Watermarked LLMs be Identified by Users via Crafted Prompts?
Aiwei Liu, Sheng Guan, Yiming Liu +6
Text watermarking for Large Language Models (LLMs) has made significant progress in detecting LLM outputs and preventing misuse. Current watermarking techniques offer high detectab…
Interpretable Contrastive Monte Carlo Tree Search Reasoning
Zitian Gao, Boye Niu, Xuzheng He +5
We propose SC-MCTS*: a novel Monte Carlo Tree Search (MCTS) reasoning algorithm for Large Language Models (LLMs), significantly improves both reasoning accuracy and speed. Our moti…
A Survey of AIOps for Failure Management in the Era of Large Language Models
Lingzhe Zhang, Tong Jia, Mengxi Jia +7
As software systems grow increasingly intricate, Artificial Intelligence for IT Operations (AIOps) methods have been widely used in software system failure management to ensure the…
Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities
Zhonghao Li, Xuming Hu, Aiwei Liu +3
Large Language Models (LLMs) are limited by their parametric knowledge, leading to hallucinations in knowledge-extensive tasks. To address this, Retrieval-Augmented Generation (RAG…
On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations
Shiao Meng, Xuming Hu, Aiwei Liu +4
Driven by the demand for cross-sentence and large-scale relation extraction, document-level relation extraction (DocRE) has attracted increasing research interest. Despite the cont…
MarkLLM: An Open-Source Toolkit for LLM Watermarking
Leyi Pan, Aiwei Liu, Zhiwei He +9
LLM watermarking, which embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text, has become crucial in mitigating the potential…
UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities
Yangning Li, Qingsong Lv, Tianyu Yu +5
Entity Set Expansion (ESE) aims to identify new entities belonging to the same semantic class as the given set of seed entities. Traditional methods solely relied on positive seed…
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments
Junzhe Chen, Xuming Hu, Shuodi Liu +4
Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence. However, existing benchmarks…
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
Xuming Hu, Xiaochuan Li, Junzhe Chen +8
Generative search engines have the potential to transform how people seek information online, but generated responses from existing large language models (LLMs)-backed generative s…
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction
Yinghui Li, Shang Qin, Haojing Huang +6
Recently, Large Language Models (LLMs) have been widely studied by researchers for their roles in various downstream NLP tasks. As a fundamental task in the NLP field, Chinese Gram…
When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models
Yinghui Li, Qingyu Zhou, Yuanzhen Luo +5
Recently, Large Language Models (LLMs) make remarkable evolutions in language understanding and generation. Following this, various benchmarks for measuring all kinds of capabiliti…