855 citations · 2.8k across the 100 of their papers we have counts for
8 papers · 2 filters
KnowCoder-X: Boosting Multilingual Information Extraction via Code
Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu +7
Empirical evidence indicates that LLMs exhibit spontaneous cross-lingual alignment. However, although LLMs show promising cross-lingual alignment in Information Extraction (IE), a…
Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework
Lu Chen, Ruqing Zhang, Jiafeng Guo +2
Retrieval-augmented generation (RAG) has emerged as a popular solution to mitigate the hallucination issues of large language models. However, existing studies on RAG seldom addres…
QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression
Wenshan Wang, Yihang Wang, Yixing Fan +2
In-context learning (ICL) capabilities are foundational to the success of large language models (LLMs). Recently, context compression has attracted growing interest since it can la…
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective
Yuchen Wen, Keping Bi, Wei Chen +2
As large language models (LLMs) become an important way of information access, there have been increasing concerns that LLMs may intensify the spread of unethical content, includin…
Class-Incremental Few-Shot Event Detection
Kailin Zhao, Xiaolong Jin, Long Bai +2
Event detection is one of the fundamental tasks in information extraction and knowledge graph. However, a realistic event detection system often needs to deal with new event classe…
A Unified Causal View of Instruction Tuning
Lu Chen, Wei Huang, Ruqing Zhang +3
Instruction tuning on a mixture of tasks has improved zero-shot capabilities in natural language processing (NLP). Nevertheless, existing methods often learn features that exhibit…
MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning
Wanqing Cui, Keping Bi, Jiafeng Guo +1
Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient…
When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation
Shiyu Ni, Keping Bi, Jiafeng Guo +1
Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge and tend to provide specious answers in such cases. Retrieval Augmenta…