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- A Survey on In-context Learning1,205
This paper presents a formal definition of ICL and clarify its correlation to related studies, and organizes and discusses advanced techniques, including training strategies, prompt designing strategies, and related analysis.
- Knowledge Neurons in Pretrained Transformers785
This paper examines the fill-in-the-blank cloze task for BERT and proposes a knowledge attribution method to identify the neurons that express the fact, finding that the activation of such knowledge neurons is positively correlated to the expression of their corresponding facts.
- Gated Self-Matching Networks for Reading Comprehension and Question Answering673
The gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage, are presented and holds the first place on the SQuAD leaderboard for both single and ensemble model.
- Double Graph Based Reasoning for Document-level Relation Extraction290
This paper proposes Graph Aggregation-and-Inference Network (GAIN) featuring double graphs, based on which GAIN first constructs a heterogeneous mention-level graph (hMG) to model complex interaction among different mentions across the document and proposes a novel path reasoning mechanism to infer relations between entities.
- Table-to-Text Generation by Structure-Aware Seq2seq Learning286
The attention visualizations and case studies show that the novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table.
- Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models263
A comprehensive and challenging benchmark specifically designed to assess LLMs' mathematical reasoning at the Olympiad level, which shows that even the most advanced models, OpenAI o1-mini and OpenAI o1-preview, struggle with highly challenging Olympiad-level problems, highlighting significant challenges in Olympiad-level mathematical reasoning.
- CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark262
The first Chinese Biomedical Language Understanding Evaluation Evaluation (CBLUE) benchmark is presented: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis.
- Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction261
A novel dependency bridge recurrent neural network (dbRNN) is proposed that simultaneously applying tree structure and sequence structure in RNN brings much better performance than only uses sequential RNN.
- Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning235
A straightforward yet effective fine-tuning technique, Child-Tuning, which updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process is proposed.
- MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning224
This paper introduces vision-language Model with Multi-Modal In-Context Learning (MMICL), a new approach to allow the VLM to deal with multi-modal inputs efficiently and constructs the Multi-modal In-Context Learning (MIC) dataset, designed to enhance the VLM's ability to understand complex multi-modal prompts.
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- Towards Time-Aware Knowledge Graph Completion184
A novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts and achieves the state-of-the-art on temporal facts consistently.
- Max-Margin Tensor Neural Network for Chinese Word Segmentation169
A novel neural network model for Chinese word segmentation called Max-Margin Tensor Neural Network (MMTNN), which has the ability to model complicated interactions between tags and context characters and a new tensor factorization approach is proposed to speed up the model and avoid overfitting.
- Graph-based Dependency Parsing with Bidirectional LSTM155
A neural network model for graph-based dependency parsing which utilizes Bidirectional LSTM (BLSTM) to capture richer contextual information instead of using high-order factorization, and enable the model to use much fewer features than previous work.
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- A Soft-label Method for Noise-tolerant Distantly Supervised Relation Extraction150
This work introduces an entity-pair level denoise method which exploits semantic information from correctly labeled entity pairs to correct wrong labels dynamically during training, and proposes a joint score function which combines the relational scores based on the entity- Pair representation and the confidence of the hard label to obtain a new label.
- Exploiting collaborative filtering techniques for automatic assessment of student free-text responses144
The experimental results show that the feature-based matrix factorization model outperforms the baseline models and the model with a re-ranking phase can achieve a better and competitive performance -- 63.6% overall accuracy on the Beetle dataset.
- Encoding Temporal Information for Time-Aware Link Prediction136
A novel time-aware KB embedding approach taking advantage of the happening time of facts is proposed, which uses temporal order constraints to model transformation between time-sensitive relations and enforce the embeddings to be temporally consistent and more accurate.
- Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker134
GIT introduces a Tracker module to track the extracted events and hence capture the interdependency among the events in a document to solve the two challenges of document-level event extraction.
- Order-Planning Neural Text Generation From Structured Data117
This paper proposes an order-planning text generation model, where order information is explicitly captured by link-based attention and a self-adaptive gate combines the link- based attention with traditional content-based Attention.
- Incorporating Glosses into Neural Word Sense Disambiguation107
GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods.
- SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction105
This paper designs a new and straightforward form of logical reasoning module that can cover more logical reasoning chains and outperforms the previous state-of-the-art methods on document-level relation extraction.
- Discourse Parsing with Attention-based Hierarchical Neural Networks102
Experimental results show that the proposed attention-based hierarchical neural network model for discourse parsing obtains comparable performance to the contemporary state-of-the-art systems with little manual feature engineering.
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- An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling73
SD decomposes the span matching problem into a series of span-level procedures, mainly including enhanced span representation, class prototype aggregation and span conflicts resolution, which achieves the new state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS.
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 2026-10-15. 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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