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- Xuanjing HuangSuggested from co-authorship
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Works113 from public data
- 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.
- 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.
- 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.
- 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.
- Query Rewriting for Retrieval-Augmented Large Language Models317
A new framework, Rewrite-Retrieve-Read instead of the previous retrieve-then-read for the retrieval-augmented LLMs from the perspective of the query rewriting, which is proven effective and scalable, and brings a new framework for retrieval-AUgmentedLLM.
- 260
- Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter198
A novel deep recurrent neural network model is proposed to combine keywords and context information to perform keyphrases from tweets and the experimental results showed that the proposed method performs significantly better than previous methods.
- 192
- Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network139
This work proposes a co-attention network incorporating textual and visual information to recommend hashtags for multimodal tweets and demonstrates that the proposed method can achieve better performance than state-of-the-art methods using textual information only.
- Joint Type Inference on Entities and Relations via Graph Convolutional Networks127
A new paradigm for the task of joint entity relation extraction is developed that first identifies entity spans, then performs a joint inference on entity types and relation types and is competitive with the state-of-the-art in relation performance.
- Retweet Prediction with Attention-based Deep Neural Network120
A novel attention-based deep neural network to incorporate contextual and social information for this task and experimental results showed that the proposed method could achieve better results than the previous state-of-the-art methods.
- Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models103
Synthetic prompting is introduced, a method that leverages a few handcrafted examples to prompt the model to generate more examples by itself, and selects effective demonstrations to elicit better reasoning.
- 100
- Not All Tokens Are What You Need for Pretraining100
This work proposes a new language model called R HO -1, which employs Selective Language Modeling (SLM), which selectively trains on useful tokens that aligned with the desired distribution, increasing both data efficiency and performance of the language model pre-training.
- AutoVerus: Automated Proof Generation for Rust Code93
A benchmark suite of 150 non-trivial proof tasks is built, which shows that AutoVerus can automatically generate correct proof for more than 90% of them, with more than half of them tackled in less than 30 seconds or 3 LLM calls.
- 92
- Mask Attention Networks: Rethinking and Strengthen Transformer83
A novel understanding of SAN and FFN are presented as Mask Attention Networks (MANs) and it is shown that they are two special cases of MANs with static mask matrices, which limit the capability for localness modeling in text representation learning.
- GLGE: A New General Language Generation Evaluation Benchmark75
The General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks, is presented and a leaderboard with strong baselines including MASS, BART, and ProphetNet is built.
- From intention to implementation: automating biomedical research via LLMs65
This paper introduces BioResearcher, the first end-to-end automated system designed to streamline the entire biomedical research process involving dry lab experiments, and demonstrates significant potential to reduce researchers’ workloads and accelerate biomedical discoveries.
- Optimizing Large Language Model Training Using FP4 Quantization64
This work introduces the first FP4 training framework for LLMs, addressing two key challenges with two key innovations: a differentiable quantization estimator for precise weight updates and an outlier clamping and compensation strategy to prevent activation collapse.
- DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation59
A new dialog pre-training framework called DialogVED is proposed, which introduces continuous latent variables into the enhanced encoder-decoder pre- training framework to increase the relevance and diversity of responses.
- RikiNet: Reading Wikipedia Pages for Natural Question Answering59
This paper introduces a new model, called RikiNet, which reads Wikipedia pages for natural question answering, which is the first single model that outperforms the single human performance.
- ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation56
The ProphetNet family pre-training models, named ProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on, are presented, and achieve new state-of-the-art performance on 10 benchmarks.
- Automated Proof Generation for Rust Code via Self-Evolution54
This paper introduces SAFE, a framework that overcomes the lack of human-written proofs to enable automated proof generation of Rust code and improves the capability of open-source models, initially unacquainted with formal verification, to automatically write proofs for Rust code.
- Retweet Behavior Prediction Using Hierarchical Dirichlet Process54
This work proposes a novel method using non-parametric statistical models to combine structural, textual, and temporal information together to predict retweet behavior and demonstrates that the proposed method can achieve better performance than state-of-the-art methods.
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 2026-10-14. 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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