Papers by Xinyan Li
AdaMix: Adaptive Mixing for Short and Long Reasoning Adapters (2026.acl-long)
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| Challenge: | Existing methods for large reasoning models have improved efficiency but still face limitations such as conflicting objectives and limited adaptability. |
| Approach: | They propose an adaptive reasoning framework that applies a uniform, computation-intensive deep reasoning strategy to all problems. |
| Outcome: | The proposed framework reduces the average response length of DeepSeek-R1-Distill-Qwen-7B by 54.9% while improving accuracy by up to 4.8% on five mathematical datasets. |
GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs (2026.acl-long)
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Weidong Tang, Jierui Li, Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, Yang You, Wangbo Zhao
| Challenge: | Existing models for general intelligence fail to model how mental states interact and crystallize into group-level outcomes. |
| Approach: | They propose a multimodal benchmark for group-level Theory of Mind (ToM) to probe nonlinear collective behavior. |
| Outcome: | The proposed model performs significantly below human levels, exposing blind spots in modeling social structures and nonlinear collective behavior. |
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Existing factual consistency metrics are often uncontrollably generating text that is factually inconsistent with inputs. |
| Approach: | They propose a weakly supervised framework that is directly trained on actual generated samples from language models with weakly annotated labels. |
| Outcome: | The proposed framework improves on the TRUE benchmark by 3.3% over existing methods with 435M parameters. |
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change (2022.emnlp-main)
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| Challenge: | Existing methods to improve neural language models perform poorly on emerging data. |
| Approach: | They propose a lexical-level masking strategy to post-train a neural language model using static data from past years. |
| Outcome: | The proposed method outperforms existing methods on two pre-trained language models, two classification tasks, and four benchmark datasets. |
Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)
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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin
| Challenge: | Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization. |
| Approach: | They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach. |
| Outcome: | The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks. |
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning (2021.acl-long)
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| Challenge: | Existing pre-training methods focus on single-modal tasks or multi-modal ones . large-scale pre- training has drawn much attention in both the community of Compute Vision (CV) and Natural Language Processing (NLP). |
| Approach: | They propose a UNIfied-MOdal pre-training architecture which can adapt to both single-modal and multi-modal understanding and generation tasks. |
| Outcome: | The proposed model can learn more generalizable representations with rich non-paired single-modal data. |
UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion (2024.acl-long)
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| Challenge: | Existing text-to-image diffusion models generate images from text prompts due to inherent brevity of textual descriptions . however, the ability to accurately synthesize images with intricate details, such as specific entities or scenes, is limited due to the inherent bribery of text descriptions. |
| Approach: | They propose a multimodal conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs. |
| Outcome: | The proposed framework excels in both text-to-image generation and zero-shot subject-driven synthesis. |
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness (2022.findings-emnlp)
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| Challenge: | Existing models of abstractive summarization are able to generate fluent and coherent summaries, but they still suffer from the unfaithful generation problem. |
| Approach: | They propose to improve the faithfulness of existing models by enhancing their factual robustness by using a novel training strategy, namely FRSUM, which teaches the model to defend against both explicit adversarial samples and implicit factual adversarials. |
| Outcome: | The proposed training strategy improves faithfulness of various models, such as T5, BART, and T5 . |
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation (2022.emnlp-main)
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| Challenge: | Existing models for text generation are weak enough to handle perturbations in inputs, leading to degeneration in faithfulness and informativeness. |
| Approach: | They propose a framework for improving faithfulness and informativeness of Seq2Seq models by perturbing word representations and word swapping. |
| Outcome: | The proposed framework improves faithfulness and informativeness of Seq2Seq models under automatic and human evaluation settings. |
Leveraging Graph to Improve Abstractive Multi-Document Summarization (2020.acl-main)
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| Challenge: | Empirical results show that our model brings substantial improvements over several strong baselines. |
| Approach: | They propose a neural abstractive multi-document summarization model which captures cross-document relations and can guide the summary generation process. |
| Outcome: | The proposed model improves on the WikiSum and MultiNews datasets and can be easily combined with pre-trained language models. |
PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions (2020.findings-emnlp)
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| Challenge: | a novel machine translation-based approach to simplify prescription directions is proposed . the language used by physicians and health professionals includes medical jargon and implicit directives . |
| Approach: | They propose a machine translation-based approach to automatically and reliably simplify prescription directions into patient-friendly language. |
| Outcome: | The proposed system achieves a BLEU score of 60.27 over 530K prescriptions from a large mail-order pharmacy. |
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling (D18-1)
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| Challenge: | Existing neural abstractive methods for document summarization are not effective for document summary. |
| Approach: | They propose to extend basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information selection process in abstractive document summarization. |
| Outcome: | The proposed model outperforms state-of-the-art methods on document summarization tasks significantly. |
InstructEval: Instruction-Tuned Text Evaluator from Human Preference (2024.findings-acl)
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| Challenge: | InstructEval is a general text evaluator based on open-source Large Language Models (LLMs). |
| Approach: | They propose to build a general multi-aspect text evaluator based on open-source Large Language Models (LLMs) they use extensive open Human Preference Modeling datasets and a small set of multi-spect annotated data to overcome the shortage of annotation resources for multi-task evaluations. |
| Outcome: | The proposed model performs comparable or superior to commercial LLMs like ChatGPT or GPT-4 in terms of both overall and multi-aspect evaluation tasks. |
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training (2024.emnlp-main)
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| Challenge: | Existing text-to-image models struggle to generate images with legible visual texts . current models lack support for Chinese texts, misspelling, and lack of diversity . |
| Approach: | They propose to empower backbone models to generate visual texts in Chinese and English . they propose to augment conventional training objective with glyph-aware training losses . |
| Outcome: | The proposed methods can generate visual texts in English and Chinese while maintaining image generation quality. |
SgSum:Transforming Multi-document Summarization into Sub-graph Selection (2021.emnlp-main)
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| Challenge: | Existing extractive multi-document summarization methods score each sentence individually and extract salient sentences one by one. |
| Approach: | They propose a novel framework for extractive multi-document summarization that selects a sub-graph as the summary instead of selecting salient sentences. |
| Outcome: | The proposed framework improves on existing methods on multi-document datasets and human evaluations show it produces more coherent and informative summaries. |
AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)
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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han
| Challenge: | Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention. |
| Approach: | They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction. |
| Outcome: | The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components. |
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning (2022.findings-acl)
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| Challenge: | Existing methods for vision-language pre-training can only learn from aligned image-caption data and rely heavily on expensive regional features. |
| Approach: | They propose an end-to-end unified-modal pre-training framework for joint learning . they propose to conduct grounded learning on both images and texts via a sharing grounded space . |
| Outcome: | The proposed model improves visual and visual semantic alignment on images and texts. |
BASS: Boosting Abstractive Summarization with Unified Semantic Graph (2021.acl-long)
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| Challenge: | Abstractive summarization for long-document or multi-document remains challenging for Seq2Seq as it does not analyze long-distance relations in text. |
| Approach: | They propose a framework for Boosting Abstractive Summarization based on a unified Semantic graph which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. |
| Outcome: | The proposed framework improves document representation and summary generation process by leveraging the graph structure. |
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing (2021.emnlp-main)
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| Challenge: | Existing approaches to parse text-to-SQL data are lacking labeled data for unseen evaluation databases. |
| Approach: | They propose a framework for enhancing SQL queries by automatically producing large numbers of SQL queries based on an abstract syntax tree grammar. |
| Outcome: | The proposed framework can produce high-quality natural language questions over strong baselines. |
Improving Neural Abstractive Document Summarization with Structural Regularization (D18-1)
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| Challenge: | Recent advances in document summarization fail to capture long-term structure of documents and multi-sentence summaries, resulting in information loss and repetitions. |
| Approach: | They propose to leverage structural information of documents and multi-sentence summaries to improve document summarization performance. |
| Outcome: | The proposed model outperforms state-of-the-art models on document summarization tasks. |