Language Models are computational models designed to understand and generate human language. These models are trained on large amounts of textual data and learn the patterns, relationships, and structures inherent in language. They can be used for various natural language processing tasks, including machine translation, sentiment analysis, text summarization, and more.


Improving Language-Based Object Detection by Explicit Generation of Negative Examples

The recent progress in language-based object detection with an open-vocabulary can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations. Training from image captions with grounded bounding boxes (ground truth or pseudo-labeled) enable the models to reason over an open-vocabulary and understand object descriptions in free-form text. In this work, we investigate the role of negative captions for training such language-based object detectors. While the fixed label space in standard object detection datasets clearly defines the set of negative classes, the free-form text used for language-based detection makes the space of potential negatives virtually infinite in size. We propose to leverage external knowledge bases and large-language-models to automatically generate contradictions for each caption in the training dataset. Furthermore, we leverage image-generate tools to create corresponding negative images to the contradicting caption. Such automatically generated data constitute hard negative examples for language-based detection and improve the model when trained from. Our experiments demonstrate the benefits of the automatically generated training data on two complex benchmarks.

Dynamic Prompting: A Unified Framework for Prompt Tuning

It has been demonstrated that prompt tuning is highly effective in efficiently eliciting knowledge from language models (LMs). However, the prompt tuning still lags behind fine tuning, especially when the LMs are small. P tuning v2 (Liu et al., 2021b) makes it comparable with finetuning by adding continuous prompts for every layer of the pre trained model. However, prepending fixed soft prompts for all instances, regardless of their discrepancy, is doubtful. In particular, the inserted prompt position, length, and the representations ofprompts for diversified instances through different tasks could all affect the prompt tuning performance. To fill this gap, we propose dynamic prompting (DP): the position, length, and prompt representation can all be dynamically optimized with respect to different tasks and instances. We conduct comprehensive experiments on the SuperGlue benchmark tovalidate our hypothesis and demonstrate substantial improvements. We also derive a unified framework for supporting our dynamic prompting strategy. In particular, we use a simple learning network and Gumble Softmax for learning instance dependent guidance. Experimental results show that simple instance level position aware soft prompts can improve the classification accuracy of up to 6 points on average on five datasets, reducing its gap with fine tuning. Besides, we also prove its universal usefulness under full data, few shot, andmultitask regimes. Combining them together can even further unleash the power of DP, narrowing the distance between fine tuning.