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Found 15 papers, 10 papers with code
Cross-Modal Consistency in Multimodal Large Language Models
Recent developments in multimodal methodologies have marked the beginning of an exciting era for models adept at processing diverse data types, encompassing text, audio, and visual content.
MIO: A Foundation Model on Multimodal Tokens
In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
Action Controlled Paraphrasing
To address the inference gap, we introduce an optional action token as a placeholder that encourages the model to determine the appropriate action independently when users' intended actions are not provided.
Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation
In text-to-image generation, using negative prompts, which describe undesirable image characteristics, can significantly boost image quality.
Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond
Expanding on our findings, we introduce "Vision Description Prompting," a method that effectively improves performance in challenging vision-related tasks.
UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation
We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders.
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework
In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.
Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs
In this work, we propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings.
Interactive Natural Language Processing
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence.
RoChBert: Towards Robust BERT Fine-tuning for Chinese
Despite of the superb performance on a wide range of tasks, pre-trained language models (e. g., BERT) have been proved vulnerable to adversarial texts.
Text Editing as Imitation Game
Text editing, such as grammatical error correction, arises naturally from imperfect textual data.
Counterfactual Adversarial Learning with Representation Interpolation
Deep learning models exhibit a preference for statistical fitting over logical reasoning.
Incorporating External POS Tagger for Punctuation Restoration
Punctuation restoration is an important post-processing step in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Recurrent Inference in Text Editing
In neural text editing, prevalent sequence-to-sequence based approaches directly map the unedited text either to the edited text or the editing operations, in which the performance is degraded by the limited source text encoding and long, varying decoding steps.
Revisit Systematic Generalization via Meaningful Learning
Humans can systematically generalize to novel compositions of existing concepts.
