Search Results for author:
Found 15 papers, 12 papers with code
FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models
The rapid development of generative AI is a double-edged sword, which not only facilitates content creation but also makes image manipulation easier and more difficult to detect.
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
Long-context models (LCMs) have made remarkable strides in recent years, offering users great convenience for handling tasks that involve long context, such as document summarization.
MemLong: Memory-Augmented Retrieval for Long Text Modeling
This work introduces MemLong: Memory-Augmented Retrieval for Long Text Generation, a method designed to enhance the capabilities of long-context language modeling by utilizing an external retriever for historical information retrieval.
OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities. However, their massive number of parameters leads to high deployment requirements and incurs significant inference costs, which impedes their practical applications.
Rethinking Negative Instances for Generative Named Entity Recognition
In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains via instruction tuning, by adopting entity-centric schema.
StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model's ability to capture the true semantic representation of visual scenes.
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting
By leveraging the augmenting data from the GEC models themselves in the post-training process and introducing regularization data for cycle training, our proposed method can effectively improve the model robustness of well-trained GEC models with only a few more training epochs as an extra cost.
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models
Graphic layout generation, a growing research field, plays a significant role in user engagement and information perception.
CMD: a framework for Context-aware Model self-Detoxification
In view of this, we introduce a Context-aware Model self-Detoxification~(CMD) framework that pays attention to both the context and the detoxification process, i. e., first detoxifying the context and then making the language model generate along the safe context.
Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!
Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space.
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
To this end, We build a system called \textbf{Visual ChatGPT}, incorporating different Visual Foundation Models, to enable the user to interact with ChatGPT by 1) sending and receiving not only languages but also images 2) providing complex visual questions or visual editing instructions that require the collaboration of multiple AI models with multi-steps.
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change
Existing methods mainly perform continual training to mitigate such a misalignment.
Chinese grammatical error correction based on knowledge distillation
In view of the poor robustness of existing Chinese grammatical error correction models on attack test sets and large model parameters, this paper uses the method of knowledge distillation to compress model parameters and improve the anti-attack ability of the model.
