Papers by Xilai Li
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to few-shot relation extraction require training. |
| Approach: | They propose a method for few-shot relation extraction using large language models, called CoT-ER, chain-of-thought with explicit evidence reasoning. |
| Outcome: | The proposed approach achieves competitive performance compared to the fully-supervised state-of-the-art approach on the FewRel1.0 and FewRela2.0 datasets. |
Masked Audio Text Encoders are Effective Multi-Modal Rescorers (2023.findings-acl)
Copied to clipboard
| Challenge: | Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition systems. |
| Approach: | They propose a multi-modal masked language model rescorer which integrates acoustic representations into the input space of MLM. |
| Outcome: | The proposed model reduces word error rate (WER) by 4%-16% on in-domain and 3%-7% on out-of-domain datasets over the text-only baseline. |
Personalizing LLMs with Binary Feedback: A Preference-Calibrated Optimization Framework (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods focus on isolated user histories, neglecting the essential role of inter-user differences. |
| Approach: | They propose a framework that personalizes Large Language Models via preference-calibrated binary signals. |
| Outcome: | The proposed framework outperforms baselines in a variety of personalization tasks and backbone LLMs. |
From Experts to Bases: Orthogonal Subspace Mixture for Continual Multimodal Instruction Tuning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing parameter-efficient approaches to multimodal Continual Instruction Tuning suffer from knowledge interference and inefficient capacity expansion, limiting scalability. |
| Approach: | They propose a framework for multimodal Continual instruction tuning that decomposes adaptation weights into a globally shared pool of orthonormal bases to capture task-invariant knowledge. |
| Outcome: | Experiments show that MoBLoRA outperforms state-of-the-art methods while maintaining superior parameter efficiency. |
Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination (2026.acl-long)
Copied to clipboard
| Challenge: | Large Vision-Language Models (LVLMs) are capable of processing visual inputs, but are susceptible to hallucinations. |
| Approach: | They propose a method to localize and localize specific visual tokens, which are defined as **Inert Tokens**, across layers, revealing a rigid semantic collapse. |
| Outcome: | The proposed approach reduces the likelihood of LVLMs being hijacked by visual inputs while maintaining general capabilities. |