Papers by Minglai Shao
A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation. |
| Approach: | They present a comprehensive synthesis of large language models and their applications . they dissect a four-module agent architecture and review representative designs . |
| Outcome: | The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities . |
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent research on slot filling has witnessed considerable improvement with considerable data and label shifts. |
| Approach: | They propose an adaptive end-to-end metric learning scheme for zero-shot slot filling that uses context-aware soft label representations and slot-level contrastive representation learning to mitigate the data and label shift problems. |
| Outcome: | The proposed approach outperforms existing methods on public benchmarks and shows that it is simple, efficient and generalizable. |