855 citations · 2.8k across the 100 of their papers we have counts for
30 papers · 1 filter
Event Ontology Expansion via LLM-Based Conceptualization
Weicheng Ren, Zixuan Li, Long Bai +3
Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology.. Existing methods typically cluste…
Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation
Shanshan Lyu, Yiwei Wang, Yujun Cai +2
Dense retrieval ranks one query vector against one document vector. On long documents, this interface can fail when a short but decisive span is weakened during document encoding b…
Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration
Guangxin Wu, Hao Zhang, Zhang Zhibin +2
Large Language Models (LLMs) have achieved remarkable success across a wide spectrum of natural language processing tasks. However, their ever-growing scale introduces significant…
Estimating Commonsense Plausibility through Semantic Shifts
Wanqing Cui, Wei Huang, Keping Bi +2
Commonsense plausibility estimation is critical for evaluating language models (LMs), yet existing generative approaches--reliant on likelihoods or verbalized judgments--struggle w…
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…
RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation
Run-Ze Fan, Yixing Fan, Jiangui Chen +3
Automatic mainstream hashtag recommendation aims to accurately provide users with concise and popular topical hashtags before publication. Generally, mainstream hashtag recommendat…
ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction
Zhilei Hu, Zixuan Li, Daozhu Xu +5
Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes,…
Nested Event Extraction upon Pivot Element Recogniton
Weicheng Ren, Zixuan Li, Xiaolong Jin +6
Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elem…
Inducing Causal Structure for Abstractive Text Summarization
Lu Chen, Ruqing Zhang, Wei Huang +3
The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spuriou…
Semantic Structure Enhanced Event Causality Identification
Zhilei Hu, Zixuan Li, Xiaolong Jin +4
Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually e…
Rich Event Modeling for Script Event Prediction
Long Bai, Saiping Guan, Zixuan Li +3
Script is a kind of structured knowledge extracted from texts, which contains a sequence of events. Based on such knowledge, script event prediction aims to predict the subsequent…
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction
Yequan Wang, Xiang Li, Aixin Sun +3
Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(…
CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks
Jiangui Chen, Ruqing Zhang, Jiafeng Guo +3
Knowledge-intensive language tasks (KILT) usually require a large body of information to provide correct answers. A popular paradigm to solve this problem is to combine a search sy…
GERE: Generative Evidence Retrieval for Fact Verification
Jiangui Chen, Ruqing Zhang, Jiafeng Guo +2
Fact verification (FV) is a challenging task which aims to verify a claim using multiple evidential sentences from trustworthy corpora, e.g., Wikipedia. Most existing approaches fo…
Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile
Hainan Zhang, Yanyan Lan, Jiafeng Guo +2
Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem i…
ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation
Hainan Zhang, Yanyan Lan, Liang Pang +2
In multi-turn dialogue generation, response is usually related with only a few contexts. Therefore, an ideal model should be able to detect these relevant contexts and produce a su…
Outline Generation: Understanding the Inherent Content Structure of Documents
Ruqing Zhang, Jiafeng Guo, Yixing Fan +2
In this paper, we introduce and tackle the Outline Generation (OG) task, which aims to unveil the inherent content structure of a multi-paragraph document by identifying its potent…