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Works401 from public data
- How to Fine-Tune BERT for Text Classification?1,861
A general solution for BERT fine-tuning is provided and new state-of-the-art results on eight widely-studied text classification datasets are obtained.
- Pre-trained models for natural language processing: A survey1,752
This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks.
- A survey of transformers1,591
This survey provides a comprehensive review of various Transformer variants and proposes a new taxonomy of X-formers from three perspectives: architectural modification, pre-training, and applications.
- Pre-trained models: Past, present and future1,134
A deep look into the history of pre-training, especially its special relation with transfer learning and self-supervised learning, is taken to reveal the crucial position of PTMs in the AI development spectrum.
- 1,008
- BERT-ATTACK: Adversarial Attack Against BERT Using BERT936
This paper proposes a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT against its fine-tuned models and other deep neural models for downstream tasks and successfully misleads the target models to predict incorrectly.
- Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence748
This paper constructs an auxiliary sentence from the aspect and converts ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI), and fine-tune the pre-trained model from BERT.
- Adversarial Multi-task Learning for Text Classification666
This paper proposes an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other, and shows that the shared knowledge learned can be regarded as off-the-shelf knowledge and easily transferred to new tasks.
- The rise and potential of large language model based agents: a survey656
A comprehensive survey on LLM-based agents is conducted, covering their construction frameworks, application scenarios, and the exploration of societies built upon LLM-based agents.
- QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization534
This work defines a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and introduces QMSum, a new benchmark for this task.
- Extractive Summarization as Text Matching529
This paper forms the extractive summarization task as a semantic text matching problem, in which a source document and candidate summaries will be matched in a semantic space to create a semantic matching framework.
- FLAT: Chinese NER Using Flat-Lattice Transformer459
Experiments show FLAT outperforms other lexicon-based models in performance and efficiency and can fully leverage the lattice information and has an excellent parallel ability.
- InternLM2 Technical Report440
InternLM2 is introduced, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques.
- A Unified Generative Framework for Aspect-based Sentiment Analysis382
Every subtask target is redefined as a sequence mixed by pointer indexes and sentiment class indexes, which converts all ABSA subtasks into a unified generative formulation and exploits the pre-training sequence-to-sequence model BART to solve all ABsa subtasks in an end- to-end framework.
- A Unified Generative Framework for Various NER Subtasks377
This work proposes to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework, and can leverage the pre-trained Seq1Seq model to solve all three kinds of N ER subtasks without the special design of the tagging schema or ways to enumerate spans.
- LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models318
This work comprehensively analyzed multiple state-of-the-art VLA models and revealed consistent brittleness beneath apparent competence, challenging the assumption that high benchmark scores equate to true competency and highlighting the need for evaluation practices that assess reliability under realistic variation.
- Heterogeneous Graph Neural Networks for Extractive Document Summarization306
This paper presents a heterogeneous graph-based neural network for extractive summarization (HETERSUMGRAPH), which contains semantic nodes of different granularity levels apart from sentences that act as the intermediary between sentences and enrich the cross-sentence relations.
- Star-Transformer296
Star-Transformer replaces the fully-connected structure with a star-shaped topology, in which every two non-adjacent nodes are connected through a shared relay node, and complexity is reduced from quadratic to linear, while preserving the capacity to capture both local composition and long-range dependency.
- Long Short-Term Memory Neural Networks for Chinese Word Segmentation293
A novel neural network model for Chinese word segmentation is proposed, which adopts the long short-term memory (LSTM) neural network to keep the previous important information in memory cell and avoids the limit of window size of local context.
- Secrets of RLHF in Large Language Models Part I: PPO290
The PPO-max, an advanced version of PPO algorithm, is explored, to efficiently improve the training stability of the policy model, and a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT is performed.
- GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge289
This paper construct context-gloss pairs and propose three BERT based models for WSD and fine-tune the pre-trained BERT model to achieve new state-of-the-art results on WSD task.
- Convolutional neural tensor network architecture for community-based question answering279
This paper proposes a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer, which outperforms the other methods on two matching tasks.
- 242
- VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks231
VLABench is an open-source benchmark for evaluating universal LCM task learning and provides high-quality training data collected via an automated framework incorporating heuristic skills and prior information to support the downstream finetuning.
- CoLAKE: Contextualized Language and Knowledge Embedding228
The Contextualized Language and Knowledge Embedding (CoLAKE) is proposed, which jointly learns contextualized representation for both language and knowledge with the extended MLM objective, and achieves surprisingly high performance on a synthetic task called word-knowledge graph completion, which shows the superiority of simultaneously contextualizing language andknowledge representation.
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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