Xiaojun Wan

As large language models become standard backends for content generation, practical provenance increasingly requires multi-bit watermarking. In provider-internal deployments, a key requirement is message symmetry: the message itself should not systematically affect either text quality or verifica...

Training Large Vision-Language Models (LVLMs) is costly and resource-intensive, making them valuable assets. To prevent malicious users from unauthorized commercialization of these artificial intelligence assets through fine-tuning and black-box deployment, model fingerprinting techniques aimed a...

Diffusion Large Language Models (DLLMs) generate text via iterative masked-token denoising, supporting parallel prediction and bidirectional context modeling. Despite these advantages, decoding remains challenging: many tokens appear predictable early, yet single-step predictions are often unstab...

Autoregressive language models are trained exclusively left-to-right. We explore the complementary factorization, training right-to-left at scale, and ask what reasoning patterns emerge when a model conditions on future context to predict the past.We train LEDOM, an open-source purely reverse aut...

As large language models (LLMs) demonstrate remarkable capabilities across a wide range of tasks, ensuring the safety of their outputs is increasingly critical. To mitigate the risk of policy-violating responses, numerous guardrail models have been developed for harmful-content detection. While e...

While DLMs have emerged as an alternative to ARMs, robust content provenance mechanisms for this architecture remain unexplored. Existing multi-bit watermarking schemes, heavily reliant on the sequential context of ARMs, cannot be directly applied to DLMs. In this paper, we reframe the multi-bit...

While large language models have achieved remarkable success in various natural language processing tasks, their potential in grammatical error correction remains underexplored. Recent work has applied reinforcement learning with rule-based rewards to CGEC, but these approaches rely on coarse-gra...

The increasing reliance on natural language generation (NLG) models, particularly large language models, has raised concerns about the reliability and accuracy of their outputs. A key challenge is hallucination, where models produce plausible but incorrect information. As a result, hallucination...

Current large language models (LLMs) often suffer from hallucination issues, i,e, generating content that appears factual but is actually unreliable. A typical hallucination detection pipeline involves response decomposition (i.e., claim extraction), query generation, evidence collection (i.e., s...

The proliferation of Large Vision-Language Models (LVLMs) has exacerbated concerns regarding model misappropriation and license violations. Malicious users may deploy open-source models as black boxes and falsely claim ownership, sparking significant community interest in fingerprinting technique...

Evaluation is important for multimodal generation tasks, while traditional multimodal evaluation metrics suffer from several limitations. With the rapid progress of MLLMs, there is growing interest in applying MLLMs to build general evaluation systems. However, existing researches often simply co...

Automatic evaluation is crucial yet challenging for open-ended natural language generation, especially when rule-based metrics are infeasible. Compared with traditional methods, the recent LLM-as-a-Judge paradigms enable better and more flexible evaluation, and show promise as generative reward m...