Papers by Minqian Liu
Domain Generalizable AI Guardrails with Augmented Policy Training (2026.acl-long)
Copied to clipboard
| Challenge: | Current guardrails overfit the training policies, preventing adaptation to new domains and policies. |
| Approach: | They propose a training recipe that uses a suite of policy perturbation strategies to reduce overfitting and increase generalization to guardrails. |
| Outcome: | The proposed training recipe reduces overfitting and increases generalization on unseen policies and achieves comparable or better performance than existing 8B guardrails on unsen policies. |
Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for learning incrementally do not address the problem of class-incremental learning. |
| Approach: | They propose a framework that can continuously learn new classes from a data stream without forgetting previously learned classes. |
| Outcome: | The proposed framework shows significant improvement over the state-of-the-art frameworks with up to 44.7% absolute F-score gain. |
Ameli: Enhancing Multimodal Entity Linking with Fine-Grained Attributes (2024.eacl-long)
Copied to clipboard
| Challenge: | Experimental results show that understanding attributes of mentions from text descriptions and visual images plays a vital role in multimodal entity linking. |
| Approach: | They propose to integrate attributes into multimodal entity linking using a text-image-based knowledge base. |
| Outcome: | The proposed approach integrates attributes into disambiguation. |
Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to improve lifelong event detection performance are limited by the limited stored examples. |
| Approach: | They propose to use Episodic Memory Prompts to explicitly retain the learned task-specific knowledge. |
| Outcome: | The proposed method can be used to update a model with new event types while retaining the capability on previously learned types. |
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Chain-of-Thought (CoT) prompting relies on the initial decisions, causing errors in early steps to accumulate and impact the final answers. |
| Approach: | They propose a divide-and-conquer style algorithm that leverages large language models to raise and answer sub-questions until collecting enough information to tackle the original one. |
| Outcome: | The proposed algorithm is more robust to errors and errors than CoT prompting and Tree-of-Thought prompting methods. |
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects (2024.naacl-long)
Copied to clipboard
| Challenge: | X-Eval is a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Approach: | They introduce a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Outcome: | The proposed framework improves the model’s ability to follow evaluation instructions and enhances the learning stage to better assess text quality. |
Holistic Evaluation for Interleaved Text-and-Image Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing evaluation benchmarks do not support arbitrarily interleaved images and text for both inputs and outputs. |
| Approach: | They propose to use a benchmark to evaluate interleaved text-and-image generation . they define five evaluation aspects for InterleavatedEval, a reference-free metric . |
| Outcome: | The proposed benchmarks cover a limited number of domains and use cases and lack comparableity-based metrics. |