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Works249 from public data
- CodeBERT: A Pre-Trained Model for Programming and Natural Languages4,421
This work develops CodeBERT with Transformer-based neural architecture, and trains it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators.
- scGPT: toward building a foundation model for single-cell multi-omics using generative AI1,440
This study has constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells, and illustrates that scGPT effectively distills critical biological insights concerning genes and cells.
- CLIP4Clip: An empirical study of CLIP for end to end video clip retrieval and captioning1,224
A CLIP4Clip model to transfer the knowledge of the CLIP model to video-language retrieval in an end-to-end manner can achieve SOTA results on various video-text retrieval datasets, including MSR-VTT, MSVC, LSMDC, ActivityNet, and DiDeMo.
- CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing1,013
A framework called CRITIC is introduced that allows LLMs, which are essentially"black boxes" to validate and progressively amend their own outputs in a manner similar to human interaction with tools.
- Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training983
After pretraining on large-scale image-caption pairs, Unicoder-VL is transferred to caption-based image-text retrieval and visual commonsense reasoning, with just one additional output layer, and shows the powerful ability of the cross-modal pre-training.
- Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models908
A system to enable the user to interact with ChatGPT by sending and receiving not only languages but also images and providing complex visual questions or visual editing instructions that require the collaboration of multiple AI models with multi-steps, and opens the door to investigating the visual roles ofChatGPT with the help of Visual Foundation Models.
- K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters648
K-Adapter is proposed, which remains the original parameters of the pre-trained model fixed and supports continual knowledge infusion and captures richer factual and commonsense knowledge than RoBERTa.
- 635
- ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training512
A new sequence-to-sequence pre-training model called ProphetNet is presented, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism that predicts the next n tokens simultaneously based on previous context tokens at each time step.
- Improving Question Answering by Commonsense-Based Pre-training489
Results show that incorporating commonsense-based function improves the baseline on three question answering tasks that require commonsense reasoning and leverages useful evidence from an external commonsense knowledge base, which is missing in existing neural network models.
- UniViLM: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation.410
Experimental results demonstrate that the UniVL can learn strong video-text representation and achieves state-of-the-art results on five downstream tasks.
- XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation404
A recent cross-lingual pre-trained model Unicoder is extended to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline and the base versions of Multilingual BERT, XLM and XLM-R are evaluated for comparison.
- 400
- NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion373
Compared to several strong baselines, N\"UWA achieves state-of-the-art results on text-to-image generation, text- to-video generation, video prediction, etc, and it also shows surprisingly good zero-shot capabilities onText-guided image and video manipulation tasks.
- ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving331
This paper proposes ToRA a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical problems by seamlessly integrating natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of tools.
- Automating code review activities by large-scale pre-training315
This research proposes CodeReviewer, a pre-trained model that utilizes four pre-training tasks tailored specifically for the code review scenario, and establishes a high-quality benchmark dataset based on the collected data for these three tasks.
- Question Generation for Question Answering299
Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.
- Progress in Neural NLP: Modeling, Learning, and Reasoning281
The importance of reasoning is emphasized in this paper because it is important for building interpretable and knowledge-driven neural NLP models to handle complex tasks.
- Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge266
A novel end-to-end graph neural model called CompareNet is proposed, which compares the news to the knowledge base (KB) through entities for fake news detection and significantly outperforms state-of-the-art methods.
- 258
- Constraint-Based Question Answering with Knowledge Graph247
A novel systematic KBQA approach to solve multi-constraint questions is proposed, which not only obtains comparable results on the two existing benchmark data-sets, but also achieves significant improvements on the ComplexQuestions.
- Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks244
It is found that doing fine-tuning on multiple languages together can bring further improvement in Unicoder, a universal language encoder that is insensitive to different languages.
- Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering232
This work proposes to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence, and achieves the state-of-the-art accuracy on the CommonsenseQA dataset.
- ReCo: Region-Controlled Text-to-Image Generation220
The proposed model, dubbed as ReCo (Region-Controlled T2I), enables the region control for arbitrary objects described by open-ended regional texts rather than by object labels from a constrained category set, and can better control the object count, spatial relationship, and region attributes such as color/size, with the free-form regional description.
- Building Task-Oriented Dialogue Systems for Online Shopping202
This work presents a general solution towards building task-oriented dialogue systems for online shopping, aiming to assist online customers in completing various purchase-related tasks, such as searching products and answering questions, in a natural language conversation manner, using existing NLP techniques, data resources, and crowdsourcing.
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 2026-10-13. 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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