Feature dynamics have emerged as a critical topic about open-environment learning due to the instability of feature availability. While traditional feature evolution targets single-label tasks, multi-label learning is essential to accommodate the exploding annotation spaces. However, multi-label classification with incremental and decremental features is a crucial yet underexplored problem, which poses the challenge of preserving feature representations and label correlations from historical instances and simultaneously adapting to newly arriving streaming data. To address these issues, we propose a two-stage, one-pass learning approach termed MLID. It attempts to compress the informative content of vanished features into the domain of survived ones, facilitate the propagation of label dependencies via low-rank regularization of the classifier, and incorporate augmented features to construct an adaptive classification mechanism. Besides, we design optimization strategies for each stage and provide theoretical guarantees of convergence. Moreover, we establish the generalization error bound of MLID and demonstrate that the compactness of the trace norm and the reuse of models based on effective features can enhance the generalization performance. Finally, we extend it to multi-shot case and extensive experimental results validate the superiority of our MLID.

In multi-hop question answering (MHQA) tasks, Chain of Thought (CoT) improves the quality of generation by guiding large language models (LLMs) through multi-step reasoning, and Knowledge Graphs (KGs) reduce hallucinations via semantic matching. Outcome Reward Models (ORMs) provide feedback after generating the final answers but fail to evaluate the process for multi-step reasoning. Traditional Process Reward Models (PRMs) evaluate the reasoning process but require costly human annotations or rollout generation. While implicit PRM is trained only with outcome signals and derives step rewards through reward parameterization without explicit annotations, it is more suitable for multi-step reasoning in MHQA tasks. However, existing implicit PRM has only been explored for plain text scenarios. When adapting to MHQA tasks, it cannot handle the graph structure constraints in KGs and capture the potential inconsistency between CoT and KG paths. To address these limitations, we propose the DPRM (Dual Implicit Process Reward Model). It trains two implicit PRMs for CoT and KG reasoning in MHQA tasks. Both PRMs, namely KG-PRM and CoT-PRM, derive step-level rewards from outcome signals via reward parameterization without additional explicit annotations. Among them, KG-PRM uses preference pairs to learn structural constraints from KGs. DPRM further introduces a consistency constraint between CoT and KG reasoning steps, making the two PRMs mutually verify and collaboratively optimize the reasoning paths. We also provide a theoretical demonstration of the derivation of process rewards. Experimental results show that our method outperforms 13 baselines on multiple datasets with up to 16.6% improvement on Hit@1.

Abstract Ontology matching (OM) plays an essential role in enabling semantic interoperability and integration across heterogeneous knowledge sources, particularly in biomedical domains which contain numerous complex concepts related to diseases and pharmaceuticals. This paper introduces GenOM , a large language model (LLM)-based ontology alignment framework, which enriches semantic representations of ontology concepts via generating textual definitions, retrieving alignment candidates with an embedding model, and incorporating exact lexical matching tools to improve precision. Extensive experiments conducted on the OAEI Bio-ML track demonstrate that GenOM can often achieve competitive performance, surpassing many baselines including traditional OM systems and recent LLM-based methods. Ablation studies confirm the effectiveness of semantic enrichment, highlighting the framework’s robustness and adaptability. Beyond the matching framework itself, this paper introduces a set of criteria for evaluating the quality of concept definitions that are generated, providing a more systematic basis for analysing LLM-generated descriptions.

Mathematical reasoning ability objectively reflects a language model's understanding of implicit knowledge in contexts, with logic being a prerequisite for exploring, articulating and establishing effective reasoning. Large language models (LLMs) have shown great potential in complex reasoning tasks represented by mathematical reasoning. However, existing mathematical datasets either focus on commonsense reasoning, assessing the model's knowledge application ability, or arithmetic problems with fixed calculation rules, evaluating the model's rapid learning capability. There is a lack of datasets that require solving problems solely through logical reasoning. As a result, the performance of LLMs in accurately understanding the implicit logical relationships in problems and deriving conclusions based solely on given conditions is hindered. To address this challenge, we construct a dataset specifically for multiple step reasoning tasks: Reasoning-Math (RMath). This dataset focuses on evaluating logical reasoning abilities with mathematical reasoning problems, covering typical problem types, including direct reasoning problems, hypothetical reasoning problems, and nested reasoning problems. Additionally, we design a standardized annotation scheme that transforms natural language descriptions of conditions into formal propositions. Other annotation contents include problem categories, proposition truth values, and proposition relationship types. This not only reduces biases caused by semantic misunderstandings during problem-solving, but also facilitates the incorporation of theoretically grounded logical reasoning methods to enhance reasoning abilities. Furthermore, we propose a normalization problem-solving framework based on propositional logic for RMath and design the problem-solving process for prompt tuning to guide LLMs to absorb mathematical logical theories and improving reasoning abilities. Finally, we evaluate RMath on several popular LLMs and present the corresponding results.

Multi-agent collaborative tasks exhibit exceptional capabilities in natural language applications and generation. By prompting agents to assign clear roles, it is possible to facilitate cooperation and achieve complementary capabilities among LLMs. A common strategy involves adopting a relatively general role assignment mechanism, such as introducing a “judge” or a “summarizer”. However, these approaches lack task-specific role customization based on task characteristics. Another strategy involves decomposing the task based on domain knowledge and task characteristics, followed by assigning appropriate roles according to LLMs’ respective strengths, such as programmers and testers. However, in some given tasks, obtaining domain knowledge related to task characteristics and getting the strengths of different LLMs is hard. To solve these problems, we propose a Multi-LLM Cooperation (MLC) framework with automatic role assignment capabilities. The core idea of the MLC is to initialize role assignments randomly and then allow the role embeddings to be learned jointly with the downstream task. To capture the state transitions of multiple LLMs during turn-based speaking, the role embedding is sequence-aware. At the same time, to avoid role convergence, the role differentiation module in MLC encourages behavioral differentiation between LLMs while ensuring the LLM team consistency, guiding different LLMs to develop complementary strengths from the optimization level. Our experiments on seven datasets demonstrate that MLC significantly enhances collaboration and expertise, which collaboratively addresses multi-agent tasks.

Graph-enhanced large language models (LLMs) leverage LLMs’ remarkable ability to model language and use graph structures to capture topological relationships. Existing graph-enhanced LLMs typically retrieve similar subgraphs to augment LLMs, where the subgraphs carry the entities related to our target and relations among the entities. However, the retrieving methods mainly focus solely on accurately matching subgraphs between our target subgraph and the candidate subgraphs at the same scale, neglecting that the subgraphs with different scales may also share similar semantics or structures. To tackle this challenge, we introduce a graph-enhanced LLM with multi-scale retrieval (MSG-LLM). It captures similar graph structures and semantics across graphs at different scales and bridges the graph alignment across multiple scales. The larger scales maintain the graph’s global information, while the smaller scales preserve the details of fine-grained sub-structures. Specifically, we construct a multi-scale variation to dynamically shrink the scale of graphs. Further, we employ a graph kernel search to discover subgraphs from the entire graph, which essentially achieves multi-scale graph retrieval in Hilbert space. Additionally, we propose to conduct multi-scale interactions (message passing) over graphs at various scales to integrate key information. The interaction also bridges the graph and LLMs, helping with graph retrieval and LLM generation. Finally, we employ a Chain-of-Thought-based LLM prediction to perform the downstream tasks. We evaluate our approach on two graph-based downstream tasks and the experimental results show that our method achieves state-of-the-art performance.

Unsupervised text style transfer aims to modify the style of a sentence while preserving its content without parallel corpora. Existing approaches attempt to separate content from style, but some words contain both content and style information. It makes them difficult to disentangle, where unsatisfactory disentanglement results in the loss of the content information or the target style. To address this issue, researchers adopted a “cycle reconstruction” mechanism to maintain content information, but it is still hard to achieve satisfactory content preservation due to incomplete disentanglement. In this paper, we propose a new disentanglement-based method, StyleFlow, which effectively avoids the loss of contents through a better cycle reconstruction via a reversible encoder. The reversible encoder is a normalizing flow that can not only produce output given input but also infer the exact input given the output reversely. We design a stack of attention-aware coupling layers, where each layer is reversible and adopts the attention mechanism to improve the content-style disentanglement. Moreover, we propose a data augmentation method based on normalizing flow to enhance the training data. Our experiments on sentiment transfer and formality transfer tasks show that StyleFlow outperforms strong baselines on both content preservation and style transfer.

Watermarking enables people to determine whether the text is generated by a specific model.It injects a unique signature based on the "green-red" list that can be tracked during detection, where the words in green lists are encouraged to be generated.Recent researchers propose to fix the green/red lists or increase the proportion of green tokens to defend against paraphrasing attacks.However, these methods cause degradation of text quality due to semantic disparities between the watermarked text and the unwatermarked text.In this paper, we propose a semantic-aware watermark method that considers contexts to generate a semantic-aware key to split a semantically balanced green/red list for watermark injection.The semantic balanced list reduces the performance drop due to adding bias on green lists.To defend against paraphrasing attacks, we generate the watermark key considering the semantics of contexts via locally sensitive hashing.To improve the text quality, we propose to split green/red lists considering semantics to enable the green list to cover almost all semantics.We also dynamically adapt the bias to balance text quality and robustness.The experiments show our advantages in both robustness and text quality comparable to existing baselines.

Dialog systems excel in collaborative tasks such as booking and shopping. However, in non-collaborative domains such as persuasion and negotiation, where system goals may conflict with user intentions, real-time dialogue strategy adaptation is necessary based on the user’s state. Current methods often overlook the user’s state and system strategy selection, instead directly imitating human discourse through end-to-end learning. To address this challenge, we introduce DiffuStra, a novel method for generating non-collaborative dialogues based on diffusion models. DiffuStra dynamically selects dialogue strategies and incorporates them into the discourse generation process through the forward and backward passes of the diffusion model, treating it as a conditional generation task. DiffuStra employs iterative denoising and reconstruction to learn the nuanced relationship between strategies and discourse, avoiding catastrophic forgetting that previous models were prone to. Our experiments demonstrate that DiffuStra outperforms baselines in non-collaborative dialogue tasks. It selects strategies that align with user states and produces coherent discourse.

The recent progress in graph representation learning boosts the development of many graph classification tasks, such as protein classification and social network classification. One of the mainstream approaches for graph representation learning is the hierarchical pooling method. It learns the graph representation by gradually reducing the scale of the graph, so it can be easily adapted to large-scale graphs. However, existing graph pooling methods discard the original graph structure during downsizing the graph, resulting in a lack of graph topological structure. In this paper, we propose a multi-scale graph neural network (MSGNN) model that not only retains the topological information of the graph but also maintains the key-subgraph for better interpretability. MSGNN gradually discards the unimportant nodes and retains the important subgraph structure during the iteration. The key subgraphs are first chosen by experience and then adaptively evolved to tailor specific graph structures for downstream tasks. The extensive experiments on seven datasets show that MSGNN improves the SOTA performance on graph classification and better retains key subgraphs.

As massive conversation examples are easily accessible on the Internet, we are now able to organize large-scale conversation corpora to build chatbots in a data-driven manner. Multi-modal social chatbots produce conversational utterances according to both textual utterances and vision signals. Due to the difficulty of bridging different modalities, the dialogue generation model of chatbots falls into local minima that only capture the mapping between textual input and textual output, as a result, it almost ignores the non-textual signals. Further, similar to the dialogue model with plain text as input and output, the generated responses from multi-modal dialogue also lack diversity and informativeness. In this paper, to address the above issues, we propose a Multi-View Meta-Learning (MultiVML) algorithm that groups samples in multiple views and customizes generation models to different groups. We employ a multi-view clustering to group the training samples so as to attend more to the unique information in non-textual modality. Tailoring different sets of model parameters for each group boosts the genereation diversity via meta-learning. We evaluate MultiVML on two variants of the OpenViDial benchmark datasets. The experiments show that our model not only better explore the information from multiple modalities, but also excels baselines in both quality and diversity.

Citing and describing related literature are crucial to scientific writing. Many existing approaches show encouraging performance in citation recommendation, but are unable to accomplish the more challenging and onerous task of citation text generation. In this paper, we propose a novel disentangled representation based model DisenCite to automatically generate the citation text through integrating paper text and citation graph. A key novelty of our method compared with existing approaches is to generate context-specific citation text, empowering the generation of different types of citations for the same paper. In particular, we first build and make available a graph enhanced contextual citation dataset (GCite) with 25K edges in different types characterized by citation contained sections over 4.8K research papers. Based on this dataset, we encode each paper according to both textual contexts and structure information in the heterogeneous citation graph. The resulted paper representations are then disentangled by the mutual information regularization between this paper and its neighbors in graph. Extensive experiments demonstrate the superior performance of our method comparing to state-of-the-art approaches. We further conduct ablation and case studies to reassure that the improvement of our method comes from generating the context-specific citation through incorporating the citation graph.

Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao, Yinhe Zheng, Dongkyu Lee, Yiping Song, Jian Sun, Nevin Zhang. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Conditional sentence generation aims to generate proper target sentences with the given condition, and has shown great promise in many text generation applications such as dialogue systems and poetry generation. The ensemble of retrieval and generation-based models retrieve texts according to the input condition to assist the generation-based model. Those approaches obtain great performance tasks as they can absorb both merits to generate informative and coherent sentences. However, the input condition and its retrieved results are usually not highly consistent due to the quality of retrieval. It leads to a retrieval bias between the condition and its retrieved result, and then text generation augmented by such results becomes unreliable. To fix this issue, we propose RBAEM, a Retrieval Bias Aware Ensemble Model. RBAEM employs two CVAEs (Conditional variational Auto-encoder) to represent the retrieved target and the ground truth with latent vectors, and then diminishes the bias by decreasing the distance of two corresponding distributions. The extensive experiments on two tasks show that the proposed methods excel the existing state-of-the-art generation models.

Large amounts of user behavior data provide opportunities for user behavior modeling and have great potential in many downstream applications such as advertising and anomaly detection. Compared with traditional methods, embedding-based methods are used more often recently because of their efficiency and scalability. These methods build a “behavior-entity” bipartite graph and learn static embeddings for nodes in the graph. However, behavior patterns in the real world could not be static because entity properties such as user interests usually evolve along with time. In this paper, we formulate user behaviors as a temporal event sequence and propose a stream network embedding approach to capture the evolving nature of user behaviors. Representation of each event is built and used to update the embeddings of nodes. Two contextual behavior modeling tasks are studied for dynamic user behaviors, and experimental results with real-world data demonstrate the effectiveness of our proposed approach over several competitive baselines.

Personalized conversation models (PCMs) generate responses according to speaker preferences. Existing personalized conversation tasks typically require models to extract speaker preferences from user descriptions or their conversation histories, which are scarce for newcomers and inactive users. In this paper, we propose a few-shot personalized conversation task with an auxiliary social network. The task requires models to generate personalized responses for a speaker given a few conversations from the speaker and a social network. Existing methods are mainly designed to incorporate descriptions or conversation histories. Those methods can hardly model speakers with so few conversations or connections between speakers. To better cater for newcomers with few resources, we propose a personalized conversation model (PCM) that learns to adapt to new speakers as well as enabling new speakers to learn from resource-rich speakers. Particularly, based on a meta-learning based PCM, we propose a task aggregator (TA) to collect other speakers' information from the social network. The TA provides prior knowledge of the new speaker in its meta-learning. Experimental results show our methods outperform all baselines in appropriateness, diversity, and consistency with speakers.

Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems.Existing methods tend to use the meta-learning framework which pre-trains the parameters on all non-target tasks then fine-tunes on the target task.However, fine-tuning distinguishes tasks from the parameter perspective but ignores the model-structure perspective, resulting in similar dialogue models for different tasks.In this paper, we propose an algorithm that can customize a unique dialogue model for each task in the few-shot setting.In our approach, each dialogue model consists of a shared module, a gating module, and a private module.The first two modules are shared among all the tasks, while the third one will differentiate into different network structures to better capture the characteristics of the corresponding task.The extensive experiments on two datasets show that our method outperforms all the baselines in terms of task consistency, response quality, and diversity.

Neural conversation models are known to generate appropriate but non-informative responses in general.A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document.In previous work, the external document is utilized by (1) creating a contextaware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory.In this paper, we propose to create the document memory with some anticipated responses in mind.This is achieved using a teacher-student framework.The teacher is given the external document, the context, and the ground-truth response, and learns how to build a response-aware document memory from three sources of information.The student learns to construct a response-anticipated document memory from the first two sources, and the teacher's insight on memory creation.Empirical results show that our model outperforms the previous stateof-the-art for the CbR task.

Typically, neural conversation systems generate replies based on the sequence-to-sequence (seq2seq) model. seq2seq tends to produce safe and universal replies, which suffers from the lack of diversity and information. Determinantal Point Processes (DPPs) is a probabilistic model defined on item sets, which can select the items with good diversity and quality. In this paper, we investigate the diversity issue in two different aspects, namely query-level and system-level diversity. We propose a novel framework which organically combines seq2seq model with Determinantal Point Processes (DPPs). The new framework achieves high quality in generated reply and significantly improves the diversity among them. Experiments show that our model achieves the best performance among various baselines in terms of both quality and diversity.

Human-computer conversation systems have attracted much attention in Natural Language Processing. Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (namely a query ) in a large conversational repository and return a reply that best matches the query. Generative approaches synthesize new replies. Both ways have certain advantages but suffer from their own disadvantages. We propose a novel ensemble of retrieval-based and generation-based conversation system. The retrieved candidates, in addition to the original query, are fed to a reply generator via a neural network, so that the model is aware of more information. The generated reply together with the retrieved ones then participates in a re-ranking process to find the final reply to output. Experimental results show that such an ensemble system outperforms each single module by a large margin.

To establish an automatic conversation system between human and computer is regarded as one of the most hardcore problems in computer science. It requires interdisciplinary techniques in information retrieval, natural language processing, and data management, etc. The challenges lie in how to respond like a human, and to maintain a relevant, meaningful, and continuous conversation. The arrival of big data era reveals the feasibility to create such a system empowered by data-driven approaches. We can now organize the conversational data as a chat companion. In this paper, we introduce a chat companion system, which is a practical conversation system between human and computer as a real application. Given the human utterances as queries, our proposed system will respond with corresponding replies retrieved and highly ranked from a massive conversational data repository. Note that 'practical' here indicates effectiveness and efficiency: both issues are important for a real-time system based on a massive data repository. We have two scenarios of single-turn and multi-turn conversations. In our system, we have a base ranking without conversational context information (for single-turn) and a context-aware ranking (for multi-turn). Both rankings can be conducted either by a shallow learning or deep learning paradigm. We combine these two rankings together in optimization. In the experimental setups, we investigate the performance between effectiveness and efficiency for the proposed methods, and we also compare against a series of baselines to demonstrate the advantage of the proposed framework in terms of [email protected], MAP, and nDCG. We present a new angle to launch a practical online conversation system between human and computer.

Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main gist of the reply. We then propose seq2BF, a “sequence to backward and forward sequences” model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply.

In human-computer conversation systems, the context of a userissued utterance is particularly important because it provides useful background information of the conversation.However, it is unwise to track all previous utterances in the current session as not all of them are equally important.In this paper, we address the problem of session segmentation.We propose an embedding-enhanced TextTiling approach, inspired by the observation that conversation utterances are highly noisy, and that word embeddings provide a robust way of capturing semantics.Experimental results show that our approach achieves better performance than the TextTiling, MMD approaches.

To establish an automatic conversation system between humans and computers is regarded as one of the most hardcore problems in computer science, which involves interdisciplinary techniques in information retrieval, natural language processing, artificial intelligence, etc. The challenges lie in how to respond so as to maintain a relevant and continuous conversation with humans. Along with the prosperity of Web 2.0, we are now able to collect extremely massive conversational data, which are publicly available. It casts a great opportunity to launch automatic conversation systems. Owing to the diversity of Web resources, a retrieval-based conversation system will be able to find at least some responses from the massive repository for any user inputs. Given a human issued message, i.e., query, our system would provide a reply after adequate training and learning of how to respond. In this paper, we propose a retrieval-based conversation system with the deep learning-to-respond schema through a deep neural network framework driven by web data. The proposed model is general and unified for different conversation scenarios in open domain. We incorporate the impact of multiple data inputs, and formulate various features and factors with optimization into the deep learning framework. In the experiments, we investigate the effectiveness of the proposed deep neural network structures with better combinations of all different evidence. We demonstrate significant performance improvement against a series of standard and state-of-art baselines in terms of [email protected], MAP, nDCG, and MRR for conversational purposes.