14 accepted papers
The task of “unlearning” certain concepts in large language models (LLMs) has gained attention for its role in mitigating harmful, private, or incorrect outputs. Current evaluations mostly rely on behavioral tests, without monitoring residual knowledge in model parameters, which can be adversarially
Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions. To address this gap, we introduce CultSportQA , a benchmark designed to assess LMs’ understanding of traditional sports across 60 countries and 6 continents, encom
Large language models (LLMs) have made significant strides in text-to-SQL tasks; however, small language models (SLMs) are crucial due to their low resource consumption and efficient inference for real-world deployment. Due to resource limitations, SLMs struggle to accurately interpret natural langu…
Continual learning is vital for task-oriented dialogue systems (ToDs), and AdapterCL, equipped with residual adapters, has proven effectiveness in this domain. However, its performance is limited by training separate adapters for each task, preventing global knowledge sharing. To address this, we pr…
Relation triplet extraction is a fundamental task in natural language processing that aims to identify semantic relationships between entities in text. It is particularly challenging in the zero-shot setting, i.e., zero-shot relation triplet extraction (ZeroRTE), where the relation sets between trai…
Catastrophic forgetting poses a significant challenge in continual learning (CL). In the context of Natural Language Processing, generative-based rehearsal CL methods have made progress in avoiding expensive retraining. However, generating pseudo samples that accurately capture the task-specific di…
Fine-tuning-based unlearning methods prevail for erasing targeted harmful, sensitive, or copyrighted information within large language models while preserving overall capabilities. However, the true effectiveness of the methods is unclear. In this paper, we delve into the limitations of fine-tuning-…
Event detection (ED), detecting events with specified types observed in given texts, is critical to many downstream applications. Existing ED methods generally require high-quality triggers annotated by human experts, which is labor-intensive, especially for those nontrivial texts about breaking eve…
Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critical step in the temporal complex event schema induction task. Existing methods effectively address this task from a graph…
Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspectbased sentiment analysis as it provides a complete aspect-level sentiment structure. However, existing ASQP datasets are usually small and low-density, hindering technical advancement. To expand the capacity, i…
Subgraph matching is a fundamental building block for graph-based applications and is challenging due to its high-order combinatorial nature. Existing studies usually tackle it by combinatorial optimization or learning-based methods. However, they suffer from exponential computational costs or searc…
Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures. However, these methods inherit issues from the conventional NAS methods, such as high computational cost and optimization difficulty. Mor…
Pretrained models have produced great success in both Computer Vision (CV) and Natural Language Processing (NLP). This progress leads to learning joint representations of vision and language pretraining by feeding visual and linguistic contents into a multi-layer transformer, Visual-Language Pretrai…
It is desirable to include more controllable attributes to enhance the diversity of generated responses in open-domain dialogue systems. However, existing methods can generate responses with only one controllable attribute or lack a flexible way to generate them with multiple controllable attribute…