27 accepted papers
Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alternative to discrete diffusion models. A line of recent work has studied samplers for discrete diffusion models, such as tau-leaping and Euler solver. However, …
Latent Action Models (LAMs) enable the learning of world models from unlabeled video by inferring abstract actions between consecutive frames. However, LAMs face a fundamental trade-off between action abstraction and generation fidelity. Existing methods typically circumvent this issue by using two-…
Guidance provides a simple and effective framework for posterior sampling by steering the generation process towards the desired distribution. When modeling discrete data, existing approaches mostly focus on guidance with the first-order Taylor approximation to improve the sampling efficiency. Howev…
Discrete flow models offer a powerful framework for learning distributions over discrete state spaces and have demonstrated superior performance compared to the discrete diffusion models. However, their convergence properties and error analysis remain largely unexplored. In this work, we develop a u…
Navigation in complex environments relies on internal spatial representations that guide action. While the brain employs a diverse repertoire of spatial tuning cells—including grid, place, and head-direction cells—a normative theory linking these static neural codes to the dynamic process of navigat…
Large Language Models demonstrate strong reasoning capabilities, which can be effectively compressed into smaller models. However, existing datasets and fine-tuning approaches still face challenges that lead to catastrophic forgetting, particularly for models smaller than 8B. First, most datasets ty
The rapid advancement of large language models (LLMs) has blurred the line between AI-generated and human-written text. This progress brings societal risks such as misinformation, authorship ambiguity, and intellectual property concerns, highlighting the urgent need for reliable AI-generated text de…
The rapid advancement of Large Language Models (LLMs) poses significant challenges for safety evaluation. Current static datasets struggle to identify emerging vulnerabilities due to three limitations: (1) they risk being exposed in model training data, leading to evaluation bias; (2) their limited…
Large Language Models (LLMs) demonstrate human-level or even superior language abilities, effectively modeling syntactic structures, yet the specific computational units responsible remain unclear. A key question is whether LLM behavioral capabilities stem from mechanisms akin to those in the human…
Large Language Models (LLMs) excel in various domains but pose inherent privacy risks. Existing methods to evaluate privacy leakage in LLMs often use memorized prefixes or simple instructions to extract data, both of which well-alignment models can easily block. Meanwhile, Jailbreak attacks bypass L…
The rapid advancement of large language models has raised significant concerns regarding their potential misuse by malicious actors. As a result, developing effective detectors to mitigate these risks has become a critical priority. However, most existing detection methods focus excessively on detec…
A text corpus centered on events is foundational to research concerning the detection, representation, reasoning, and harnessing of online events. The majority of current event-based datasets mainly target sentence-level tasks, thus to advance event-related research spanning from sentence to documen…
Document-level event argument extraction is a crucial task to help understand event information. Existing methods mostly ignore the different extraction difficulties of arguments, and the lack of task planning significantly affects the extraction and reasoning abilities of the model. In this paper,…
Event argument extraction is critical to various natural language processing tasks for providing structured information. Existing works usually extract the event arguments one by one, and mostly neglect to build dependency information among event argument roles, especially from the perspective of ev…
Recent studies have shown the effectiveness of retrieval augmentation in many generative NLP tasks. These retrieval-augmented methods allow models to explicitly acquire prior external knowledge in a non-parametric manner and regard the retrieved reference instances as cues to augment text generation…
Empathy is a key ability for a human-like dialogue system. Inspired by social psychology, empathy includes both affective and cognitive aspects. Previous works on this topic have merely focused on recognizing emotions or modeling cognition with commonsense knowledge. Nevertheless, the generated resu…
Knowledge graphs (KGs) have received increasing attention due to its wide applications on natural language processing. However, its use case on temporal question answering (QA) has not been well-explored. Most of existing methods are developed based on pre-trained language models, which might not be…
Dense retrieval is widely used for entity linking to retrieve entities from large-scale knowledge bases. Mainstream techniques are based on a dual-encoder framework, which encodes mentions and entities independently and calculates their relevances via rough interaction metrics, resulting in difficul…
A major barrier to deploying current machine learning models lies in their non-reliability to dataset shifts. To resolve this problem, most existing studies attempted to transfer stable information to unseen environments. Particularly, independent causal mechanisms-based methods proposed to remove m…
Transforming the large amounts of unstructured text on the Internet into structured event knowledge is a critical, yet unsolved goal of NLP, especially when addressing document-level text. Existing methods struggle in Document-level Event Extraction (DEE) due to its two intrinsic challenges: (a) Nes…
Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), a trained model can still correctly predict t from (h, r, ?), or h from (?, r, t…
Although the navigation of robots in urban environments has achieved great performance, there is still a problem of insufficient robustness in cross-scene (ground, water surface) navigation applications. An intuitive idea is to introduce multi-modal complementary data to improve the robustness of th
For sentence-level extractive summarization, there is a disproportionate ratio of selected and unselected sentences, leading to flatting the summary features when maximizing the accuracy. The imbalanced classification of summarization is inherent, which can’t be addressed by common algorithms easily…
Sentence-level extractive summarization is a fundamental yet challenging task, and recent powerful approaches prefer to pick sentences sorted by the predicted probabilities until the length limit is reached, a.k.a. ``Top-K Strategy''. This length limit is fixed based on the validation set, resulting…
Reading text to extract relational facts has been a long-standing goal in natural language processing. It becomes especially challenging when the extraction scope is extended to document level, where multiple entities in a document generally exhibit complex intra- and inter-sentence relations. In th…
To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities. Although the human effort is reduced, the generated incomplete and noisy annotations pose new challenges for learning effective n…
Non-orthogonal multiple access (NOMA) is one of the most promising technologies in fifth-generation mobile communication system for its advantages in serving multiuser simultaneously and enhancing spectrum efficiency. In this paper, we investigate the optimization problem of sum-rate maximization fo…