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Works86 from public data
- Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences704
It is argued that it is crucial to consider the incomplete nature of KG when incorporating it into recommender system, and a new translation-based recommendation model, which specially accounts for various preferences in translating a user to an item, and jointly train it with a KG completion model by combining several transfer schemes.
- Sequential Recommendation with User Memory Networks555
A memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation is designed, which store and update users» historical records explicitly, which enhances the expressiveness of the model.
- Benchmarking Foundation Models with Language-Model-as-an-Examiner249
A novel benchmarking framework where the LM serves as a knowledgeable examiner that formulates questions based on its knowledge and evaluates responses in a reference-free manner is proposed, allowing for effortless extensibility as various LMs can be adopted as the examiner and the questions can be constantly updated given more diverse trigger topics.
- Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction193
An effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there is a lack of training data is proposed.
- Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate171
This work introduces a formal debate framework (FORD) to conduct a three-stage debate among LLMs with real-world scenarios alignment: fair debate, mismatched debate, and roundtable debate to examine whether LLMs can collaborate effectively to achieve a consensus for a shared goal.
- Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval155
A Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos to facilitate video retrieval with complex queries, thereby achieving a better video retrieval performance.
- Improving Event Detection via Open-domain Trigger Knowledge113
A novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations is proposed.
- Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding108
A novel Multi-Prototype Mention Embedding model is proposed, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base, and an efficient language model based approach to disambiguate each mention to a specific sense.
- Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen87
A new task of expertise style transfer is proposed and a manually annotated dataset is contributed with the goal of alleviating cognitive biases and improving the accuracy and expertise level of laymen descriptions using simple words.
- Low-Resource Name Tagging Learned with Weakly Labeled Data86
A novel neural model for name tagging solely based on weakly labeled (WL) data is proposed, so that it can be applied in any low-resource settings and demonstrate superior performance as well as efficiency.
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- Who, Where, and What to Wear?55
A novel method to automatically harvest fashion knowledge from social media by using a contextualized fashion concept learning module, which captures the dependencies and correlations among different fashion concepts and introduces a weak label modeling module which can effectively exploit machine-labeled data, a complementary of clean data.
- Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition54
A novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different unde- fined classes from the other class to improve few-shot NER and improve the dis- criminative ability of NER classifier andance the understanding of predefined classes with stand-by semantic knowledge.
- Debiasing NLU Models via Causal Intervention and Counterfactual Reasoning47
This paper shows that there is an unobserved confounder for the natural language utterances and their respective classes, leading to spurious correlations from training data, and provides a new perspective with causal inference to find out the bias.
- Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision46
This paper proposes a novel method for joint representation learning of cross-lingual words and entities that captures mutually complementary knowledge, and enables cross-lingsual inferences among knowledge bases and texts.
- Image Enhanced Event Detection in News Articles44
This paper proposes a novel Dual Recurrent Multimodal Model, DRMM, to conduct deep interactions between images and sentences for modality features aggregation and superior performance compared to six state-of-art baselines as well as further ablation studies demonstrate the significance of image modality and effectiveness of the proposed architecture.
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- Interactive Contrastive Learning for Self-Supervised Entity Alignment41
This work conducts bidirectional contrastive learning via building pseudo-aligned entity pairs as pivots to achieve direct cross-KG information interaction and exploits the integration of entity textual and structural information and elaborately designs encoders for better utilization in the self-supervised setting.
- Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks39
This survey probes the core challenges that the rise of LLMs poses for evaluation, and identifies and analyzes two pivotal transitions: from task-specific to capability-based evaluation and from manual to automated evaluation.
- Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction36
This paper proposes a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data.
- Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution35
A new task of Knowledge-aware Language Model Attribution (KaLMA) is defined that improves upon three core concerns with conventional attributed LMs, and a new ``Conscious Incompetence" setting is proposed considering the incomplete knowledge repository.
- Intuitive or Dependent? Investigating LLMs’ Behavior Style to Conflicting Prompts34
This study investigates the behaviors of Large Language Models when faced with conflicting prompts versus their internal memory to understand LLMs' decision mechanism and establish a complete benchmarking framework including a dataset, a robustness evaluation pipeline, and corresponding metrics.
- Robust Prompt Optimization for Large Language Models Against Distribution Shifts33
This work proposes Generalized Prompt Optimization framework, which incorporates the unlabeled data from the target group into prompt optimization, and demonstrates the effectiveness of the proposed framework with significant performance improvement on thetarget group and comparable performance on the source group.
- Discriminative Reasoning with Sparse Event Representation for Document-level Event-Event Relation Extraction32
This paper proposes a novel DECI model (SENDIR) for better document-level reasoning, different from existing works that build an event graph via linguistic tools, which introduces supportive event pairs for joint modeling.
- Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion32
This work presents InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns, and includes various inference patterns (e.g., reasoning path length and types) for comprehensive evaluation.
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- TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning30
This paper proposes a novel target guided dynamic mixup (TGDM) framework that leverages the target data to guide the generation of mixed images via dynamic mix up and introduces an intermediate domain generated by mixing images in the source and the target domain.
- Information Extraction in Low-Resource Scenarios: Survey and Perspective28
This paper presents a review of neural approaches to low-resource IE from traditional and LLM-based perspectives, systematically organizing them into a fine-grained taxonomy, highlighting promising applications and delineating future research directions.
- FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation25
This work constructs FOLLOWUPQG, a dataset of over 3K real-world tuples collected from a Reddit forum providing layman-friendly explanations for open-ended questions, and evaluates current question generation models on their efficacy for generating follow-up questions.
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- Improving Neural Relation Extraction with Implicit Mutual Relations24
The proposed neural RE framework provides a promising improvement for the RE task, and significantly outperforms the state-of-the-art methods, and the component for mining implicit mutual relations is so flexible that can help to improve the performance of both CNN-based and RNN-based RE models significant.
- Document-level Relation Extraction via Separate Relation Representation and Logical Reasoning22
This article proposes a novel model, SRLR, using Separate Relation Representation and Logical Reasoning considering the indirect relation representation and complex reasoning of evidence sentence problems, and proposes a logical reasoning module to identify evidence sentences and conduct relational reasoning.
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- How Knowledge Graph and Attention Help? A Qualitative Analysis into Bag-level Relation Extraction20
It is shown that a straightforward variant of RE model can achieve significant improvements on two real-world datasets as compared with three state-of-the-art baselines and that KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior.
- Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models18
This paper proposes to automate dataset updating and provides systematic analysis regarding its effectiveness in dealing with benchmark leakage issue, difficulty control, and stability, and is the first to automate updating benchmarks for reliable and timely evaluation.
- What Makes the Story Forward?18
A novel explainable FEG framework that highlights and integrates two types of event knowledge, sequential knowledge of direct event-event relations and inferential knowledge that reflects the intermediate character psychology between events, such as intents, causes, reactions, which intrinsically pushes the story forward is proposed.
- LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning17
A new data synthesis method called LogicPro, which leverages LeetCode-style algorithm \underline{Pro}blems and their corresponding corresponding \underline{Pro}gram solutions to synthesize Complex \underline{Logic}al Reasoning data in text format, outperforming a wide range of existing reasoning datasets.
- XLink: An Unsupervised Bilingual Entity Linking System17
This paper builds an online bilingual entity linking system XLink, which is based on Wikipeida and Baidu Baike and proposes an unsupervised generative probabilistic method and utilize text and knowledge joint representations to perform entity disambiguation.
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- Name List Only? Target Entity Disambiguation in Short Texts14
This paper proposes a graphbased model called TremenRank to collectively identify target entities in short texts given a name list only that propagates trust within the graph, allowing for an arbitrary number of target entities and texts using inverted index technology.
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- Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models10
SwitchCoT is proposed, an automatic framework that adaptively chooses between long and short CoT strategies to balance reasoning accuracy and computational efficiency, and is designed to be budget-aware, making it broadly applicable across scenarios with varying resource constraints.
- Good Learners Think Their Thinking: Generative PRM Makes Large Reasoning Model More Efficient Math Learner10
This work proposes a novel intrinsic signal-driven generative process evaluation mechanism operating at the thought level to address major bottlenecks in RL-based training, and introduces a capability-adaptive reward mechanism that dynamically balances exploration and exploitation based on the LRM's current proficiency.
- ChartifyText: Automated Chart Generation from Data-Involved Texts via LLM10
ChartifyText is proposed, a novel fully-automated approach that leverages Large Language Models (LLMs) to convert complex data-involved texts to expressive charts to accurately convey the underlying data and insights to readers.
- Exploring & exploiting high-order graph structure for sparse knowledge graph completion10
A novel framework, LR-GCN, is presented that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse KGC.
- Event-level Knowledge Editing8
A new task setting is proposed: event-level knowledge editing, which directly edits new events into LLMs and improves over conventional triplet-level editing on efficiency and completeness.
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- Category Multi-representation: A Unified Solution for Named Entity Recognition in Clinical Texts8
This paper proposes a novel method that learn multiple representations for each category, namely category-multi-representation (CMR) that captures the semantic relatedness between words and clinical categories from different perspectives and enjoys a lightweight adaption among languages.
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- FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion5
This work proposes a new federated learning framework that implicitly aggregates knowledge from multiple KGs without demanding raw data exchange and entity alignment, and achieves comparable performance to state-of-the-art alignment-based models without requiring any labeled alignments or raw user data sharing.
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- Target Detection and Knowledge Learning for Domain Restricted Question Answering5
This research proposes a semi-automatic domain-restricted FAQ answering framework SDFA, without relying on any external resources, and detects the targets of questions to assist both the fast domain knowledge learning and the answer retrieval.
- Complex Logical Query Answering by Calibrating Knowledge Graph Completion Models4
This paper proposes a method for calibrating KGC models, namely CKGC, which enables KGC models to adapt to answering complex logical queries and can enhance the performance of CLQA while preserving the ranking evaluation metrics of KGC models.
- To be or not to be? an exploration of continuously controllable prompt engineering4
ControlPE enables finer adjustments to prompt effects, complementing existing prompt engineering, and effectively controls continuous targets, proving to be a promising solution for control a variety of prompts.
- On Modeling Sense Relatedness in Multi-prototype Word Embedding4
A novel approach to capture word sense relatedness in multi-prototype word embedding model by introducing a random process to integrate these two types of senses and design two non-parametric methods for word sense induction.
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- Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning3
A label-free curricular meaningful learning framework that efficiently and effectively diagnoses and remedies knowledge deficiencies in LLMs, improving various LLMs across seven out-of-distribution reasoning and language understanding benchmarks, and surpasses methods that rely on labeled datasets for deficiency diagnosis.
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- VEM$$^2$$L: an easy but effective framework for fusing text and structure knowledge on sparse knowledge graph completion2
This paper proposes VEM2, a joint learning framework that incorporates structure and relevant text information to supplement insufficient features for sparse KGs, and proposes a novel co-distillation strategy using the Variational EM algorithm on unobserved queries.
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- Evolution of the TCP gene family and pan-genome analysis in tea (Camellia sinensis)1
A pangenome-informed TCP resource is established and it is suggested that dosage remodeling and structural variation may have contributed to lineage diversification in tea and provide candidates for future studies of domestication-related evolution.
- A Responsibility-Based Classification of Error Patterns in Human-Machine Collaborative Decision-Making and the Double-Edged Effect of Information Feedback1
System error types and information feedback jointly affect operator performance in human–machine collaborative decision-making offer theoretical guidance for strengthening responsibility perception and optimizing human-machine system design.
- Think straight or think again? Continual joint learning of deduction, abduction and induction1
This paper designs a three-level learning procedure for deduction, abduction and induction that can achieve comparable performance with GPT-3.5 in human evaluation and introduces induction as a fact retriever to support and guide the above dual learning.
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- Teach2Eval: An Indirect Evaluation Method for LLM by Judging How It Teaches1
Teach2Eval is introduced, an indirect evaluation framework inspired by the Feynman Technique that evaluates a model's multiple abilities to teach weaker student models to perform tasks effectively and captures a broad range of cognitive abilities that are orthogonal to current benchmarks.
- Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance1
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- Construction and Empirical Study of Personalized Teaching Model for Higher Vocational Music Performance Major Based on Intelligent Recommendation Algorithm–
This study verified the effectiveness of the intelligent recommendation algorithm in personalized teaching of higher vocational art programs, providing a replicable technical solution and practical reference for vocational education teaching reform.
- R2R-Agent: An AI-Powered Intelligent Educational System for Automated Repository Reproducibility and Technology-Enhanced Learning–
R2R-Agent (Repo-to-Run Agent), an innovative AI-powered intelligent educational system designed to automate repository reproducibility through the integration of self-repairing agent architectures and Large Language Models, is introduced.
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- Pre-training and evaluation of numeracy-oriented language model–
This work proposes two numerical pre-training methods with objectives that encourage the LM to understand the magnitude and value of numbers and encode the dependency between a number and its context, and applies the proposed methods on BERT.
- DSEL: A Domain-Specific Entity Linking System–
This paper proposes an unsupervised method to generate domain data from Wikipedia and provides a domain-specific neural collective entity linking model for each domain, and builds a system that can provide domain entity linking for users.
- A Design of Economic Mechanism for Business Development Based on Blockchain–
This work proposed a value-backed economic mechanism which has a clear separation of tokens' attributes as well as a reasonable distribution rule of profit, and a utility token with a warrant attribute is introduced to stimulate and facilitate the value exchange between miners and other participants.
- Knowledge-Enhanced Bilingual Textual Representations for Cross-Lingual Semantic Textual Similarity–
It is shown that the proposed method outperforms cross-lingual representations methods using parallel data on cross-lingsual tasks, and achieves competitive results on mono-lingUAL tasks.
- Is a Common Phrase an Entity Mention or Not? Dual Representations for Domain-Specific Named Entity Recognition–
A novel BLSTM based NER model tailored for specific domains by learning dual representations for each word is presented, which learns not only general domain knowledge derived from an external large scale general corpus via a word embedding model, but also the specific domain knowledge by training a stacked deep neural network (SDNN) integrating the results of a low-cost pre-entity-linking process.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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