Infinite-Dimensional Feature Interaction

The past neural network design has largely focused on feature representation space dimension and its capacity scaling (e.g., width, depth), but overlooked the feature interaction space scaling. Recent advancements have shown shifted focus towards element-wise multiplication to facilitate higher-dimensional feature interaction space for better information transformation. Despite this progress, multiplications predominantly capture low-order interactions, thus remaining confined to a finite-dimensional interaction space. To transcend this limitation, classic kernel methods emerge as a promising solution to engage features in an infinite-dimensional space. We introduce InfiNet, a model architecture that enables feature interaction within an infinite-dimensional space created by RBF kernel. Our experiments reveal that InfiNet achieves new state-of-the-art, owing to its capability to leverage infinite-dimensional interactions, significantly enhancing model performance.

Backdoor Removal for Generative Large Language Models

With rapid advances, generative large language models (LLMs) dominate various Natural Language Processing (NLP) tasks from understanding to reasoning. Yet, language models' inherent vulnerabilities may be exacerbated due to increased accessibility and unrestricted model training on massive textual data from the Internet. A malicious adversary may publish poisoned data online and conduct backdoor attacks on the victim LLMs pre-trained on the poisoned data. Backdoored LLMs behave innocuously for normal queries and generate harmful responses when the backdoor trigger is activated. Despite significant efforts paid to LLMs' safety issues, LLMs are still struggling against backdoor attacks. As Anthropic recently revealed, existing safety training strategies, including supervised fine-tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), fail to revoke the backdoors once the LLM is backdoored during the pre-training stage. In this paper, we present Simulate and Eliminate (SANDE) to erase the undesired backdoored mappings for generative LLMs. We initially propose Overwrite Supervised Fine-tuning (OSFT) for effective backdoor removal when the trigger is known. Then, to handle the scenarios where the trigger patterns are unknown, we integrate OSFT into our two-stage framework, SANDE. Unlike previous works that center on the identification of backdoors, our safety-enhanced LLMs are able to behave normally even when the exact triggers are activated. We conduct comprehensive experiments to show that our proposed SANDE is effective against backdoor attacks while bringing minimal harm to LLMs' powerful capability without any additional access to unbackdoored clean models. We will release the reproducible code.

Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation

Large language models (LLMs) have been widely used in various applications but are known to suffer from issues related to untruthfulness and toxicity. While parameter-efficient modules (PEMs) have demonstrated their effectiveness in equipping models with new skills, leveraging PEMs for deficiency unlearning remains underexplored. In this work, we propose a PEMs operation approach, namely Extraction-before-Subtraction (Ext-Sub), to enhance the truthfulness and detoxification of LLMs through the integration of ``expert'' PEM and ``anti-expert'' PEM. Remarkably, even anti-expert PEM possess valuable capabilities due to their proficiency in generating fabricated content, which necessitates language modeling and logical narrative competence. Rather than merely negating the parameters, our approach involves extracting and eliminating solely the deficiency capability within anti-expert PEM while preserving the general capabilities. To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modeling and mathematical reasoning. Our empirical results demonstrate that our approach effectively improves truthfulness and detoxification, while largely preserving the fundamental abilities of LLMs.

Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion

Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increase of graph sparsity. This problem is also exacerbated because of the widespread existence of sparse KGs in practical applications. To alleviate this challenge, we present a novel framework, LR-GCN, that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse KGC. The proposed approach comprises two main components: a GNN-based predictor and a reasoning path distiller. The reasoning path distiller explores high-order graph structures such as reasoning paths and encodes them as rich-semantic edges, explicitly compositing long-range dependencies into the predictor. This step also plays an essential role in densifying KGs, effectively alleviating the sparse issue. Furthermore, the path distiller further distills logical reasoning knowledge from these mined reasoning paths into the predictor. These two components are jointly optimized using a well-designed variational EM algorithm. Extensive experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.

VEM$^2$L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion

Knowledge Graph Completion has been widely studied recently to complete missing elements within triples via mainly modeling graph structural features, but performs sensitive to the sparsity of graph structure. Relevant texts like entity names and descriptions, acting as another expression form for Knowledge Graphs (KGs), are expected to solve this challenge. Several methods have been proposed to utilize both structure and text messages with two encoders, but only achieved limited improvements due to the failure to balance weights between them. And reserving both structural and textual encoders during inference also suffers from heavily overwhelmed parameters. Motivated by Knowledge Distillation, we view knowledge as mappings from input to output probabilities and propose a plug-and-play framework VEM2L over sparse KGs to fuse knowledge extracted from text and structure messages into a unity. Specifically, we partition knowledge acquired by models into two nonoverlapping parts: one part is relevant to the fitting capacity upon training triples, which could be fused by motivating two encoders to learn from each other on training sets; the other reflects the generalization ability upon unobserved queries. And correspondingly, we propose a new fusion strategy proved by Variational EM algorithm to fuse the generalization ability of models, during which we also apply graph densification operations to further alleviate the sparse graph problem. By combining these two fusion methods, we propose VEM2L framework finally. Both detailed theoretical evidence, as well as quantitative and qualitative experiments, demonstrates the effectiveness and efficiency of our proposed framework.

DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension

Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC. To fully exploit such discourse structure in multiparty dialogue, we present a discourse-aware dialogue graph neural network, DADgraph, which explicitly constructs the dialogue graph using discourse dependency links and discourse relations. To validate our model, we perform experiments on the Molweni corpus, a large-scale MRC dataset built over multiparty dialogue annotated with discourse structure. Experiments on Molweni show that our discourse-aware model achieves statistically significant improvements compared against strong neural network MRC baselines.

Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure

We present the Molweni dataset, a machine reading comprehension (MRC) dataset built over multiparty dialogues. Molweni's source samples from the Ubuntu Chat Corpus, including 10,000 dialogues comprising 88,303 utterances. We annotate 32,700 questions on this corpus, including both answerable and unanswerable questions. Molweni also uniquely contributes discourse dependency annotations for its multiparty dialogues, contributing large-scale (78,246 annotated discourse relations) data to bear on the task of multiparty dialogue understanding. Our experiments show that Molweni is a challenging dataset for current MRC models; BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7% F1 on Molweni's questions, a 20+% significant drop as compared against its SQuAD 2.0 performance.

An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension

In this paper, we propose the scheme for annotating large-scale multi-party chat dialogues for discourse parsing and machine comprehension. The main goal of this project is to help understand multi-party dialogues. Our dataset is based on the Ubuntu Chat Corpus. For each multi-party dialogue, we annotate the discourse structure and question-answer pairs for dialogues. As we know, this is the first large scale corpus for multi-party dialogues discourse parsing, and we firstly propose the task for multi-party dialogues machine reading comprehension.