Search Results for author:
Found 21 papers, 12 papers with code
SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization
Tehchniques for manipulating images are advancing rapidly; while these are helpful for many useful tasks, they also pose a threat to society with their ability to create believable misinformation.
Ranked #5 on
Image Manipulation Localization
on Columbia
LabelAId: Just-in-time AI Interventions for Improving Human Labeling Quality and Domain Knowledge in Crowdsourcing Systems
Crowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge.
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering.
PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning
Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions.
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning.
Exploring and Characterizing Large Language Models For Embedded System Development and Debugging
We leverage this finding to study how human programmers interact with these tools, and develop an human-AI based software engineering workflow for building embedded systems.
Pre-training Language Models for Comparative Reasoning
Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability.
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions
Contrast consistency, the ability of a model to make consistently correct predictions in the presence of perturbations, is an essential aspect in NLP.
Improving Language Models via Plug-and-Play Retrieval Feedback
ReFeed first generates initial outputs, then utilizes a retrieval model to acquire relevant information from large document collections, and finally incorporates the retrieved information into the in-context demonstration for output refinement, thereby addressing the limitations of LLMs in a more efficient and cost-effective manner.
Large Language Models are Built-in Autoregressive Search Engines
To overcome this limitation, recent autoregressive search engines replace the dual-encoder architecture by directly generating identifiers for relevant documents in the candidate pool.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #42 on
Code Generation
on MBPP
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach
However, applying such methods to commonsense reasoning tasks faces two unique challenges, i. e., the lack of a general large-scale corpus for retrieval and a corresponding effective commonsense retriever.
A Unified Encoder-Decoder Framework with Entity Memory
The entity knowledge is stored in the memory as latent representations, and the memory is pre-trained on Wikipedia along with encoder-decoder parameters.
On the Relationship Between Counterfactual Explainer and Recommender
To enable explainability, recent techniques such as ACCENT and FIA are looking for counterfactual explanations that are specific historical actions of a user, the removal of which leads to a change to the recommendation result.
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods
Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences.
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
A set of knowledge experts seek diverse reasoning on KG to encourage various generation outputs.
Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
In this work, we focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process.
SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization
We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations.
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.
DCA: Diversified Co-Attention towards Informative Live Video Commenting
In this paper, we aim to collect diversified information from video and text for informative comment generation.
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms.
