Dacheng Tao

In this paper, we investigate knowledge forgetting in large language models with a focus on its generalisation—ensuring that models forget not only specific training samples but also related implicit knowledge. To this end, we begin by identifying a broader unlearning scope that includes both tar...

Mixture-of-Agents (MoA) improves LLM performance through layered collaboration, but its dense topology raises costs and latency. Existing methods employ LLM judges to filter responses, yet still require all models to perform inference before judging, failing to cut costs effectively. They also la...

Knowledge distillation (KD) transfers capabilities from large language models (LLMs) to smaller students, yet it can fail unpredictably and also underpins model leakage risks. Our analysis revealed several distillation traps: tail noise, off-policy instability, and, most fundamentally, the teache...

Reliable verifiable data has become a key driver of capability gains in modern language models, enabling stable reinforcement learning with verifiable rewards and effective distillation that transfers competence across math, coding, and agentic tasks. Yet constructing generalizable synthetic veri...

Large language models (LLMs) have achieved remarkable performance across diverse tasks, largely driven by large-scale pretraining. However, this data abundance introduces test data contamination, where benchmark datasets overlap with pretraining corpora, undermining the reliability of model evalu...

The pursuit of real-time agentic interaction has driven interest in Diffusion-based Large Language Models (dLLMs) as alternatives to auto-regressive backbones, promising to break the sequential latency bottleneck. However, does such efficiency gains translate into effective agentic behavior? In t...

Membership inference attacks (MIAs) aim to determine whether specific data was used to train a model. While existing MIAs against pre-trained Large Language Models (LLMs) typically require access to complete logits (probabilities), such access is sometimes unavailable in real-world deployments wh...

Recently, long-thought reasoning LLMs, such as OpenAI’s O1, adopt extended reasoning processes similar to how humans ponder over complex problems. This reasoning paradigm significantly enhances the model’s problem-solving abilities and achieves promising results. However, long-thought reasoning p...

Large language models (LLMs) have achieved remarkable performance across a wide range of natural language processing (NLP) tasks. However, they remain susceptible to backdoor attacks, where adversaries embed hidden triggers in the input to induce malicious, attacker-specified behaviors. While exi...

Aligning Large Language Models (LLMs) to be faithful to new knowledge in complex, multi-hop reasoning tasks is a critical, yet unsolved, challenge. We find that SFT-based methods, e.g., Reason-KE, while state-of-the-art, suffer from a “faithfulness gap”: they optimize for format mimicry rather th...

Recent work has aimed to enhance the reasoning capabilities of language models, but these methods are often limited to domains with objectively verifiable answers. To overcome this limitation, we introduce Reasoning-Guided Exploration for Online DPO (RGE-DPO), a novel self-play framework designed...

Recent progress in large language models (LLMs) has boosted mathematical reasoning, yet geometry remains challenging where auxiliary construction is often essential. Prior methods either underperform or depend on very large models (e.g., GPT-4o), making them costly. We argue that reinforcement le...