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Found 21 papers, 10 papers with code
Cross-lingual Contextualized Phrase Retrieval
In our work, we propose a new task formulation of dense retrieval, cross-lingual contextualized phrase retrieval, which aims to augment cross-lingual applications by addressing polysemy using context information.
Inferflow: an Efficient and Highly Configurable Inference Engine for Large Language Models
We present Inferflow, an efficient and highly configurable inference engine for large language models (LLMs).
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation
While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of input-side multimodal comprehension, lacking the capacity for multimodal content generation.
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
We release our dataset, model, and demo to foster future research in the area of multimodal instruction following.
Ranked #89 on
Visual Question Answering
on MM-Vet
PandaGPT: One Model To Instruction-Follow Them All
To do so, PandaGPT combines the multimodal encoders from ImageBind and the large language models from Vicuna.
A Frustratingly Simple Decoding Method for Neural Text Generation
We introduce a frustratingly simple, super efficient and surprisingly effective decoding method, which we call Frustratingly Simple Decoding (FSD), for neural text generation.
Unified Text Structuralization with Instruction-tuned Language Models
Text structuralization is one of the important fields of natural language processing (NLP) consists of information extraction (IE) and structure formalization.
$N$-gram Is Back: Residual Learning of Neural Text Generation with $n$-gram Language Model
The combination of $n$-gram and neural LMs not only allows the neural part to focus on the deeper understanding of language but also provides a flexible way to customize an LM by switching the underlying $n$-gram model without changing the neural model.
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
While large-scale neural language models, such as GPT2 and BART, have achieved impressive results on various text generation tasks, they tend to get stuck in undesirable sentence-level loops with maximization-based decoding algorithms (\textit{e. g.}, greedy search).
Visualizing the Relationship Between Encoded Linguistic Information and Task Performance
To this end, we study the dynamic relationship between the encoded linguistic information and task performance from the viewpoint of Pareto Optimality.
Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics
Current practices in metric evaluation focus on one single dataset, e. g., Newstest dataset in each year's WMT Metrics Shared Task.
A Survey on Retrieval-Augmented Text Generation
Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community.
GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation
In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic.
Data Augmentation for Text Generation Without Any Augmented Data
Data augmentation is an effective way to improve the performance of many neural text generation models.
TranSmart: A Practical Interactive Machine Translation System
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed.
Neural Machine Translation with Monolingual Translation Memory
Second, the memory retriever and NMT model can be jointly optimized for the ultimate translation goal.
Assessing Dialogue Systems with Distribution Distances
An important aspect of developing dialogue systems is how to evaluate and compare the performance of different systems.
On the Branching Bias of Syntax Extracted from Pre-trained Language Models
Many efforts have been devoted to extracting constituency trees from pre-trained language models, often proceeding in two stages: feature definition and parsing.
Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
Our proposed model disentangles the image content from the visual attributes, and it learns to modify the latter using the textual description, before generating a new image from the content and the modified attribute representation.
Evaluating Explanation Methods for Neural Machine Translation
Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods.
Neural Machine Translation with Noisy Lexical Constraints
Experiments show that our approach can indeed improve the translation quality with the automatically generated constraints.
