10 accepted papers
Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-world long-context information, primarily due to insufficient long-context alignment caused by data quality issues, training inefficiencies, and the lack of w…
In the realm of large language models (LLMs), the ability of models to accurately follow instructions is paramount as more agents and applications leverage LLMs for construction, where the complexity of instructions are rapidly increasing. However, on the one hand, there is only a certain amount of…
Multi-document grounded dialogue systems (DGDS) belong to a class of conversational agents that answer users’ requests by finding supporting knowledge from a collection of documents. Most previous studies aim to improve the knowledge retrieval model or propose more effective ways to incorporate exte…
One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence. Previous works designed expansion operators to scale up pretrained models to the target model before tr…
When trying to answer complex questions, people often rely on multiple sources of information, such as visual, textual, and tabular data. Previous approaches to this problem have focused on designing input features or model structure in the multi-modal space, which is inflexible for cross-modal reas…
Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learning-based methods achieve very impressive performance on standard EM benchmarks, their real-world application performance is much frustrating. In this paper, we highlight that such the gap between real…
Task-specific fine-tuning on pre-trained transformers has achieved performance breakthroughs in multiple NLP tasks. Yet, as both computation and parameter size grows linearly with the number of sub-tasks, it is increasingly difficult to adopt such methods to the real world due to unrealistic memory…
The security of Deep Neural Networks (DNNs) is of great importance due to their employment in various safety-critical applications. DNNs are shown to be vulnerable against the Trojan attack that manipulates the model parameters via poisoned training and gets activated by the pre-defined trigger in i…
Entity resolution (ER) aims to identify data records referring to the same real-world entity. Most existing ER approaches rely on the assumption that the entity records to be resolved are homogeneous, i.e., their attributes are aligned. Unfortunately, entities in real-world datasets are often hetero…
Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the software products to gain commercial advantages. On the other hand, binary decompilation can be leveraged for code vulne…