Papers by Yida Zhao
Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models (2024.acl-long)
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| Challenge: | Syntactic Transformer language models aim to achieve better generalization through simultaneously modeling syntax trees and sentences. |
| Approach: | They propose a class of Transformer language models with explicit dependency-based inductive bias. |
| Outcome: | Experiments show that the proposed models outperform constituency-based models on sentences annotated with dependency trees and achieve better generalization. |
GiLT: Augmenting Transformer Language Models with Dependency Graphs (2026.acl-long)
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| Challenge: | Recent work focuses on syntactic tree structures of languages, in particular constituency tree structures. |
| Approach: | They propose a Graph-Infused Layers Transformer Language Model which leverages dependency graphs to augment Transformer language models. |
| Outcome: | The proposed model achieves better syntactic generalization while maintaining competitive perplexity compared with baseline models. |
EvolveSearch: An Iterative Self-Evolving Search Agent (2025.emnlp-main)
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Ding-Chu Zhang, Yida Zhao, Jialong Wu, Liwen Zhang, Baixuan Li, Wenbiao Yin, Yong Jiang, Yu-Feng Li, Kewei Tu, Pengjun Xie, Fei Huang
| Challenge: | Existing approaches to enabling LLM web search proficiency struggle with data production in open-search domains, while supervised fine-tuning struggles with data utilization efficiency. |
| Approach: | They propose an iterative self-evolution framework that combines SFT and RL to enhance agentic web search capabilities without external human-annotated reasoning data. |
| Outcome: | EvolveSearch achieves 4.7% improvement over current state-of-the-art in seven benchmarks . supervised fine-tuning struggles with data production in open-search domains compared with RL . |
A Systematic Study of Compositional Syntactic Transformer Language Models (2025.acl-long)
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| Challenge: | Syntactic language models (SLMs) incorporate syntactical biases into Transformers . authors identify key aspects of design choices in existing models and novel variants based on experimental results . |
| Approach: | They propose a framework that incorporates existing and new SLMs to enhance Transformers by incorporating syntactic biases. |
| Outcome: | The proposed framework improves on existing models and novel variants across language modeling, syntactic generalization, summarization, and inference efficiency. |
Don’t Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing rerankers are mainly trained on well-edited texts, but stylistic features can be misled by reranked models. |
| Approach: | They propose a style-augmented multi-task framework that prioritizes effective knowledge over stylistic perturbations by using an LLM to derive passage-level supervision on whether a passage helps or harms answer correctness. |
| Outcome: | Extensive experiments show that SARK improves generation performance across multiple LLMs under mixed-style conditions. |