21 accepted papers
Although Multimodal Large Language Models have made remarkable progress, they still struggle with long-video understanding due to the massive memory footprint of KV Caches. Exsiting methods often resort to disjoint retrieval or attention-based static reduction to achieve compression. However, these …
Instruction tuning plays a critical role in enhancing the performance and efficiency of Large Language Models (LLMs). Its success depends not only on the quality of the instruction data but also on the inherent capabilities of the LLM itself. Some studies suggest that even a small amount of high-qua
Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing approaches often rely on static heuristics or stochastic search, rendering them brittle against advanced safety alignment. To address this, we introduce…
Recent advancements in DeepFakes attribution technologies have significantly enhanced forensic capabilities, enabling the extraction of traces left by generative models (GMs) in images, making DeepFakes traceable back to their source GMs. Meanwhile, several attacks have attempted to evade attributio…
Thanks to their linguistic capabilities, LLMs offer an opportunity to bridge the gap between informal mathematics and formal languages through autoformalization. However, it is still unclear how well LLMs generalize to sophisticated and naturally occurring mathematical statements. To address this ga
In medical image segmentation tasks, diffusion models have exhibited significant potential. However, mainstream diffusion models show drawbacks including multiple sampling times and slow prediction results. Recently, as a standalone generative network, consistency models have resolved the existing i
As machine learning model modification techniques are extensively employed to obtain well-performing models at reduced costs, several studies have emerged to determine the presence of a modification relationship (i.e., lineage) between models. However, these methods are not robust to high-impact mod…
Retrieval-augmented generation (RAG) improves the service quality of large language models by retrieving relevant documents from credible literature and integrating them into the context of the user query.Recently, the rise of the cloud RAG service has made it possible for users to query relevant do…
U-Net is a widely used model for medical image segmentation, renowned for its strong feature extraction capabilities and U-shaped design, which incorporates skip connections to preserve critical information. However, its decoders exhibit information-specific preferences for the supplementary content…
Aligned LLMs are secure, capable of recognizing and refusing to answer malicious questions. However, the role of internal parameters in maintaining such security is not well understood yet, further these models can be vulnerable to security degradation when subjected to fine-tuning attacks. To addre…
Influence diffusion models are used for simulating information propagation in social networks. While most existing influence diffusion models are probabilistic, the emergence of Large Language Model (LLM) sheds light on the language-level inferences and interactions of user agents. This paper presen…
3D object detection plays an important role in autonomous driving; however, its vulnerability to backdoor attacks has become evident. By injecting “triggers” to poison the training dataset, backdoor attacks manipulate the detector's prediction for inputs containing these triggers. Existing backdoor…
Multi-armed bandit is a simple but powerful algorithmic framework, and many effective algorithms have been proposed for various online models. In numerous applications, the decision-maker faces diminishing marginal utility. With non-linear aggregations, those algorithms often have poor regret bound…
Autoformalization is the task of automatically translating mathematical content written in natural language to a formal language expression. The growing language interpretation capabilities of Large Language Models (LLMs), including in formal languages, are lowering the barriers for autoformalizatio…
This paper investigates the possibility of approximating multiple mathematical operations in latent space for expression derivation. To this end, we introduce different multi-operational representation paradigms, modelling mathematical operations as explicit geometric transformations. By leveraging…
Best Arm Identification (BAI) is a general online pure exploration framework to identify optimal decisions among candidates via sequential interactions. We pioneer the Optimal Arms identification with Knapsacks (OAK) problem, which extends the BAI setting to model the resource consumption. We presen…
Federated learning (FL) over mobile devices is a promising distributed learning paradigm for various mobile applications. However, practical deployment of FL over mobile devices is very challenging because (i) conventional FL incurs huge training latency for mobile devices due to interleaved local c…
The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices. A typical dilemma is: in order to provide complex intelligent services (e.g. smart city), we need inference results of multiple ML m…
Injecting desired geometric properties into text representations has attracted a lot of attention. A property that has been argued for, due to its better utilisation of representation space, is isotropy. In parallel, VAEs have been successful in areas of NLP, but are known for their sub-optimal util…
This paper studies the algorithmic stability and generalizability of decentralized stochastic gradient descent (D-SGD). We prove that the consensus model learned by D-SGD is $\mathcal{O}{(m/N\unaryplus1/m\unaryplus\lambda^2)}$-stable in expectation in the non-convex non-smooth setting, where $N$ is…
There are already some text-based visual question answering (TextVQA) benchmarks for developing machine's ability to answer questions based on texts in images in recent years. However, models developed on these benchmarks cannot work effectively in many real-life scenarios (e.g. traffic monitoring,…