Papers by Liyan Xu
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent pre-trained language models have achieved remarkable zero-shot performance . we propose a self-learning framework that utilizes unlabeled data of target languages . |
| Approach: | They propose a self-learning framework that utilizes unlabeled data of target languages to select silver labels for cross-lingual transfer tasks. |
| Outcome: | The proposed framework outperforms baseline models on two cross-lingual tasks by 10 F1 on average and 2.5 accuracy on natural language inference (NLI). |
ReFreeKV: Towards Threshold-Free KV Cache Compression (2026.findings-acl)
Copied to clipboard
| Challenge: | Towards the KV cache efficiency, we propose a new objective that lifts the threshold constraints for robust KV compression. |
| Approach: | They propose a method that adjusts KV cache budgets while preserving full-cache performance. |
| Outcome: | The proposed method can reduce memory consumption while preserving full-cache performance. |
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have focused on specialized BERT-variants and recent LLMs to reason inconsistencies. |
| Approach: | They propose to incorporate task-specific taxonomy into inferences to facilitate both zero-shot and supervised paradigms. |
| Outcome: | The proposed model outperforms specialized non-LLM and recent LLM models in a number of domains. |
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors (2023.acl-long)
Copied to clipboard
Liyan Tang, Tanya Goyal, Alex Fabbri, Philippe Laban, Jiacheng Xu, Semih Yavuz, Wojciech Kryscinski, Justin Rousseau, Greg Durrett
| Challenge: | Abstractive summarization systems still include factual errors in generated summaries despite recent improvements in factuality detection . |
| Approach: | They aggregate factuality error annotations from nine existing datasets and stratify them according to the underlying summarization model. |
| Outcome: | The proposed method improves on the ChatGPT-based model and shows that it is not superior for all error types. |
Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing work on document-level relation extraction has focused on end-to-end setting that extracts global entities and relations jointly. |
| Approach: | They propose to introduce a two-way interaction between COREF and RE that is specifically designed to leverage task characteristics, bridging decisions of two tasks for direct task interference. |
| Outcome: | The proposed model achieves the best performance by up to 2.3/5.1 F1 over the baseline. |
ScEdit: Script-based Assessment of Knowledge Editing (2025.findings-acl)
Copied to clipboard
Xinye Li, Zunwen Zheng, Qian Zhang, Dekai Zhuang, Jiabao Kang, Liyan Xu, Qingbin Liu, Xi Chen, Zhiying Tu, Dianhui Chu, Dianbo Sui
| Challenge: | Knowledge Editing (KE) has gained increasing attention, yet current evaluation frameworks do not integrate KE into real-world application scenarios. |
| Approach: | They propose a script-based benchmark which encompasses both counterfactual and temporal edits and integrates token-level and text-level evaluation methods. |
| Outcome: | The proposed method combines token-level and text-level evaluation methods with a new fact-based evaluation framework. |
Revealing the Myth of Higher-Order Inference in Coreference Resolution (2020.emnlp-main)
Copied to clipboard
| Challenge: | Adapted coreference resolution models have only marginally improved performance over representation learning. |
| Approach: | They implement an end-to-end coreference system and four HOI approaches to analyze the impact of higher-order inference on coreference resolution. |
| Outcome: | The proposed model shows that the impact of higher-order inference (HOI) on coreference resolution is negative to marginal, providing a new perspective on the task. |
Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions (2024.acl-long)
Copied to clipboard
| Challenge: | a novel graph for narrative comprehension captures coherence between passages in narratives . end-to-end paradigms are effective for comprehension tasks, but may not be sufficient for all comprehension scenarios. |
| Approach: | They propose a graph dubbed NarCo which explicitly depicts task-agnostic coherence dependencies that are ready to be consumed by downstream tasks. |
| Outcome: | The proposed graph is practically instantiated by LLMs without human annotations. |
Exploring Deductive and Inductive Reasoning Capabilities of Large Language Models in Procedural Planning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Deductive and inductive reasoning are fundamental components of human cognition . authors present a benchmark to assess their performance in procedural planning . |
| Approach: | They propose a benchmark to assess the deductive and inductive reasoning abilities of LLMs . they propose IMSE to enable LLM to generate multiple similar procedural plans . |
| Outcome: | The proposed method improves inductive reasoning abilities of LLMs, the authors show . they show that LLM models show excellent deductive reasoning capabilities but suboptimal inductive performance. |
Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach (2023.acl-long)
Copied to clipboard
| Challenge: | Existing task setting for attribute mining on e-commerce products is closed-world, but recent work has moved towards open-world aspect. |
| Approach: | They propose a task setting for attribute mining on e-commerce products that uses a high-quality seed attribute set bootstrapped from existing resources. |
| Outcome: | The proposed approach surpasses baselines on existing attributes by 12 F1 and discovers values from 39% new attributes. |
Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a limited number of text encoders are able to recognize fine-grained entities or events within encoded semantics. |
| Approach: | They propose a new evaluation dataset to examine embeddings' ability to recognize fine-grained entities or events within encoded semantics. |
| Outcome: | The proposed dataset shows embeddings struggle with fine-grained matching . the proposed encoder outperforms the state-of-the-art 7B model in a small sample . |
Situated Embedding Models for Context-Aware Dense Retrieval (2026.acl-short)
Copied to clipboard
| Challenge: | Existing embedding models are not well-equipped to encode situated context effectively, i.e., situating a chunk’s meaning within its context. |
| Approach: | They propose to represent short chunks in a way that is conditioned on a broader context window to enhance retrieval performance. |
| Outcome: | The proposed model outperforms state-of-the-art embedding models on a book-plot retrieval dataset. |
Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations (2022.starsem-1)
Copied to clipboard
| Challenge: | Existing models for online decoding on active input are not trained to handle an online decode environment. |
| Approach: | They propose a new direction of coreference resolution for online decoding on actively generated input such as dialogue . they propose to use models that accept utterances and their past context and find mentions upon each dialogue turn . |
| Outcome: | The proposed model outperforms the baseline model by 10% on three datasets: Friends, OntoNotes, and BOLT. |