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Found 13 papers, 5 papers with code
Multimodal Instruction Tuning with Conditional Mixture of LoRA
Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot generalization capabilities for unseen multimodal tasks.
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning
Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data.
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects
Natural Language Generation (NLG) typically involves evaluating the generated text in various aspects (e. g., consistency and naturalness) to obtain a comprehensive assessment.
MULTISCRIPT: Multimodal Script Learning for Supporting Open Domain Everyday Tasks
Automatically generating scripts (i. e. sequences of key steps described in text) from video demonstrations and reasoning about the subsequent steps are crucial to the modern AI virtual assistants to guide humans to complete everyday tasks, especially unfamiliar ones.
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
Chain-of-Thought (CoT) prompting enables large language models to solve complex reasoning problems by generating intermediate steps.
AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes
We propose attribute-aware multimodal entity linking, where the input is a mention described with a text and image, and the goal is to predict the corresponding target entity from a multimodal knowledge base (KB) where each entity is also described with a text description, a visual image and a set of attributes and values.
Iteratively Improving Biomedical Entity Linking and Event Extraction via Hard Expectation-Maximization
To solve these challenges, we propose joint biomedical entity linking and event extraction by regarding the event structures and entity references in knowledge bases as latent variables and updating the two task-specific models in a hard Expectation-Maximization (EM) fashion: (1) predicting the missing variables for each partially annotated dataset based on the current two task-specific models, and (2) updating the parameters of each model on the corresponding pseudo completed dataset.
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning
Our results indicate that fine-tuning the model on a diverse set of tasks and instructions leads to a reduced sensitivity to variations in instructions for each task.
Improve Event Extraction via Self-Training with Gradient Guidance
Data scarcity has been the main factor that hinders the progress of event extraction.
Identifying and Measuring Token-Level Sentiment Bias in Pre-trained Language Models with Prompts
Due to the superior performance, large-scale pre-trained language models (PLMs) have been widely adopted in many aspects of human society.
Improved Latent Tree Induction with Distant Supervision via Span Constraints
For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing.
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings
However, one of the fundamental limitations of these approaches is that they are highly dependent on image and camera settings and can only learn to map an input HSI with one specific setting to an output HSI with another.
Hyperspectral Image Super-Resolution
Image Super-Resolution
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Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction
In this paper, we describe a technique for using BibTeX to generate, automatically, a large-scale 41M labeled strings), labeled dataset, that is four orders of magnitude larger than the current largest CFE dataset, namely the UMass Citation Field Extraction dataset [Anzaroot and McCallum, 2013].
