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- Zheng ZhangSuggested from co-authorship
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Works44 from public data
- 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.
- Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs899
Deep Graph Library (DGL) enables arbitrary message handling and mutation operators, flexible propagation rules, and is framework agnostic so as to leverage high-performance tensor, autograd operations, and other feature extraction modules already available in existing frameworks.
- 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.
- 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.
- 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.
- InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output216
A versatile large-vision language model that supports long-contextual input and output, InternLM-XComposer-2.5 excels in various text-image comprehension and composition applications, achieving GPT-4V level capabilities with merely 7B LLM backend.
- Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders160
A suite of 256 SAEs, trained on each layer and sublayer of the Llama-3.1-8B-Base model, with 32K and 128K features are introduced and modifications to a state-of-the-art SAE variant, Top-K SAEs, are evaluated across multiple dimensions.
- OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection91
OriGen is a fully open-source framework that incorporates self-reflection capabilities and a novel dataset augmentation methodology for generating high-quality, large-scale RTL code, and exhibits superior capabilities in self-reflection and error correction.
- Multi-Scale Self-Attention for Text Classification72
A Multi-Scale Transformer which uses multi-scale multi-head self-attention to capture features from different scales and a strategy to control the scale distribution for each layer is designed.
- VideoRoPE: What Makes for Good Video Rotary Position Embedding?71
VideoRoPE is introduced, with a 3D structure designed to preserve spatio-temporal relationships, which consistently surpasses previous RoPE variants, across diverse downstream tasks such as long video retrieval, video understanding, and video hallucination.
- Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective60
The roadmap to achieving o1 is analyzed from the perspective of reinforcement learning, focusing on four key components: policy initialization, reward design, search, and learning, which underscore how learning and search drive o1's advancement.
- InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions52
This project draws inspiration from the concept of the Specialized Generalist AI and introduces disentangled streaming perception, reasoning, and memory mechanisms, enabling real-time interaction with streaming video and audio input.
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- Survey on Factuality in Large Language Models50
This survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs, and examines the mechanisms through which LLMs store and process facts, seeking the primary causes of factual errors.
- GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation50
This paper proposes a large-scale, general-domain dataset, GenWiki, which has 1.3M text and graph examples, respectively, and provides this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.
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- Syntax-guided text generation via graph neural network32
This paper proposes the word graph model, a graph generation problem exploiting both syntactic and word-ordering relationships that builds a sentence incrementally and maintains syntactic integrity via a syntax-driven, top-down, breadth-first generation process.
- Joint Parsing and Generation for Abstractive Summarization31
A novel neural architecture for abstractive summarization that combines a sequential decoder with a tree-based decoder in a synchronized manner to generate a summary sentence and its syntactic parse and describes a novel human evaluation protocol to assess if, and to what extent, a summary remains true to its original meanings.
- Knowledge-Centric Hallucination Detection28
RefChecker, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grained hallucinations, outperforms prior methods and is strongly aligned with human judgments.
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- NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens23
The design and construction of NovelQA is presented, highlighting its manual annotation, and diverse question types, and significant insights into the models’ performance are revealed, particularly emphasizing the challenges they face with multi-hop reasoning, detail-oriented questions, and extremely long input with more than 100,000 tokens.
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- Case2Code: Scalable Synthetic Data for Code Generation11
Experimental results show that such induction training benefits not only in distribution Case2Code performance but also enhances various coding abilities of trained LLMs, demonstrating the great potential of learning inductive reasoning.
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- ReAttention: Training-Free Infinite Context with Finite Attention Scope10
This work proposes LongCache, a training-free approach that enables LLM to support an infinite context with finite context scope, through full-context cache selection and training-free integration, which effectively frees LLMs from the length extrapolation issue.
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- Data-free Weight Compress and Denoise for Large Language Models4
This paper proposes a novel approach termed Data-free Joint Rank-k Approximation for compressing the parameter matrices of large Language Models without necessitating additional involvement of any corpus, while simultaneously preserving orthogonality in conjunction with pruning and quantization methods.
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- Text information aggregation with centrality attention3
This work proposes a new way of obtaining aggregation weights, called eigen-centrality self-attention, which builds a fully-connected graph for all the words in a sentence, then compute the eigen -centrality as the attention score of each word.
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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 2026-10-16. 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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