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Found 21 papers, 10 papers with code
Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model
To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels.
MMCode: Evaluating Multi-Modal Code Large Language Models with Visually Rich Programming Problems
Programming often involves converting detailed and complex specifications into code, a process during which developers typically utilize visual aids to more effectively convey concepts.
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility.
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Then we propose to fine-tune a small language model as the debate judge for harmfulness inference, to facilitate multimodal fusion between the harmfulness rationales and the intrinsic multimodal information within memes.
GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abuse
The exponential growth of social media has profoundly transformed how information is created, disseminated, and absorbed, exceeding any precedent in the digital age.
Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models
The age of social media is rife with memes.
VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning
Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks.
VST++: Efficient and Stronger Visual Saliency Transformer
Our previous work, the Visual Saliency Transformer (VST), addressed this constraint from a transformer-based sequence-to-sequence perspective, to unify RGB and RGB-D SOD.
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.
Ranked #3 on
Code Generation
on CodeContests
(Test Set pass@1 metric)
Augmented Large Language Models with Parametric Knowledge Guiding
We demonstrate that our PKG framework can enhance the performance of "black-box" LLMs on a range of domain knowledge-intensive tasks that require factual (+7. 9%), tabular (+11. 9%), medical (+3. 0%), and multimodal (+8. 1%) knowledge.
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval
The conventional dense retrieval paradigm relies on encoding images and texts into dense representations using dual-stream encoders, however, it faces challenges with low retrieval speed in large-scale retrieval scenarios.
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse Retrieval
To address this issue, we propose a novel sparse retrieval paradigm for ITR that exploits sparse representations in the vocabulary space for images and texts.
Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning
The spread of rumors along with breaking events seriously hinders the truth in the era of social media.
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection
Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances.
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks
In contrast, Transformer Decoder with the causal attention masks is naturally sensitive to the word order.
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning
Different to these heavy-cost models, we introduce a lightweight image captioning framework (I-Tuning), which contains a small number of trainable parameters.
A Frustratingly Simple Approach for End-to-End Image Captioning
Before training the captioning models, an extra object detector is utilized to recognize the objects in the image at first.
Analyzing the Implicit Position Encoding Ability of Transformer Decoder
A common limitation of Transformer Encoder's self-attention mechanism is that it cannot automatically capture the information of word order, so one needs to feed the explicit position encodings into the target model.
Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives
Gender bias in word embeddings gradually becomes a vivid research field in recent years.
Have Attention Heads in BERT Learned Constituency Grammar?
Our results suggest that SMS tasks decrease the average CGI ability of upper layers, while NLI tasks increase it.
Natural Language Inference
Natural Language Understanding
+2
Positional Artefacts Propagate Through Masked Language Model Embeddings
In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers.
