Papers by Yilin Shen
ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)
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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase. |
| Approach: | They propose an adaptive context refactoring framework that monitors and reshapes the interaction history to mitigate contextual inertia and state drift. |
| Outcome: | The proposed model outperforms baselines while reducing token consumption. |
Lite Unified Modeling for Discriminative Reading Comprehension (2022.acl-long)
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| Challenge: | generative and discriminative MRCs focus on answer generation, extractive MRC on answer extraction. |
| Approach: | They propose a lightweight POS-Enhanced Iterative Co-Attention Network to handle diverse discriminative MRC tasks synchronously. |
| Outcome: | The proposed model improves on four discriminative MRC benchmarks. |
Adaptive Rank Selections for Low-Rank Approximation of Language Models (2024.naacl-long)
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| Challenge: | Singular Value Decomposition (SVD) or its weighted variants has progressed in compressing language models. |
| Approach: | They propose a binary masking mechanism for optimizing the number of ranks in a differentiable framework. |
| Outcome: | The proposed algorithm achieves much better accuracy than previous SVD and its weighted variants. |
Generating Dialogue Responses from a Semantic Latent Space (2020.emnlp-main)
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| Challenge: | Existing models for dialogue generation are unable to integrate information from multiple semantically similar valid responses of a given prompt. |
| Approach: | They propose to learn the pair relationship between the prompts and responses as a regression task instead of the end-to-end classification on vocabulary. |
| Outcome: | The proposed model learns the pair relationship between the prompts and responses on a latent space instead of the end-to-end classification on vocabulary. |
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection (N19-1)
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| Challenge: | Using a pre-defined vocabulary is a common approach to selecting text inputs . however, using a large vocabulary is not economical, as it limits the model's applicability on computation-or memoryconstrained scenarios. |
| Approach: | They propose a more sophisticated variational vocabulary dropout to perform vocabulary selection . they propose two new metrics to measure area under accuracy-vocab curve and Vocab Size under X% accuracy drop . |
| Outcome: | The proposed framework outperforms the baselines on the vocabulary selection problem on multiple NLP classification tasks. |
FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing (2025.naacl-long)
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James Seale Smith, Chi-Heng Lin, Shikhar Tuli, Haris Jeelani, Shangqian Gao, Yilin Shen, Hongxia Jin, Yen-Chang Hsu
| Challenge: | Empirical evaluations demonstrate substantial performance gains over existing methods . |
| Approach: | They propose a method to prune LLMs that selectively prunes model blocks based on an importance score and replaces them with a low-parameter replacement strategy. |
| Outcome: | The proposed method achieves state-of-the-art performance on 5/6 and 6/6 benchmarks with a compression rate of 30% and 40%. |
Fast Domain Adaptation of Semantic Parsers via Paraphrase Attention (D19-61)
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| Challenge: | Semantic parsers are used to convert user’s natural language commands to executable logical form in intelligent personal agents. Labeled datasets required to train such parser are expensive to collect, and are never comprehensive. |
| Approach: | They propose to use a sequence-to-sequence/tree attention based attention-based sequence-based parsers which support fast near real time retraining. |
| Outcome: | The proposed parsers can maintain high accuracy and fast retraining time while leveraging paraphrases already present in the training dataset. |
Numerical Optimizations for Weighted Low-rank Estimation on Language Models (2022.emnlp-main)
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| Challenge: | Singular value decomposition (SVD) is one of the most popular methods for estimating a target matrix with smaller matrices. |
| Approach: | They propose a method that approximates a target matrix with smaller matrices by two smaller . they also propose metric to predict when the SVD may introduce a significant performance drop. |
| Outcome: | The proposed method can perform better than current SOTA methods in compressing Transformer-based language models. |
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (N18-2)
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| Challenge: | Intent detection and slot filling are two main tasks for building a spoken language understanding system. |
| Approach: | They propose to use a sequence to sequence model to generate both intent and slot filling tasks together to perform the two tasks jointly. |
| Outcome: | The proposed model achieves 0.5% intent accuracy improvement and 0.9 % slot filling improvement on the ATIS benchmark data. |
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling (2024.naacl-long)
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| Challenge: | Rapid explosion in model sizes has resulted in high inference times . open-source LLMs are democratizing research in natural language processing . |
| Approach: | They propose a suite of multi-token prediction language models that reduce net inference times by leveraging traditional autoregressive weights. |
| Outcome: | The proposed model achieves same-quality generated text as baseline (Pythia-6.9B) with only 5.87% and 2.67% parameter and training time overheads. |
RSDA: Restoring Stale Data Affinity via Dynamic Renovation Strategy for Mitigating Data Scarcity (2026.acl-long)
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Yidan Liang, Jia Zhu, Weijie Shi, Hanghui Guo, Yue Cui, Jiawei Shen, Guoqing Ma, Jingjiang Liu, Qingyu Niu, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | High-quality data is the cornerstone of advancing large language models, but the supply of premium data is nearing depletion, while vast stale corpora remain underutilized. |
| Approach: | They propose a framework to restore stale data affinity by quantifying the latent value of samples and employing a dynamic renovation strategy selection mechanism to determine the optimal component-level strategy. |
| Outcome: | The proposed framework achieves performance improvements using less than 10% of the data volume, underscoring that the latent potential of stale corpora remains largely untapped. |
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs’ Instruction Following Capability (2026.findings-acl)
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Jiaming Wang, Yunke Zhao, Peng Ding, Jun Kuang, Yibin Shen, Zhe Tang, Yilin Jin, ZongYu Wang, Xiaoyu Li, Xuezhi Cao
| Challenge: | Existing models lack the ability to adhere to instructions, resulting in suboptimal performance. |
| Approach: | They propose an automated iterative instruction-following benchmark with integrated feedback mechanism. |
| Outcome: | The proposed benchmark identifies erroneous components in model responses and provides feedback accurately. |
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding (2021.naacl-main)
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| Challenge: | Existing continual learning approaches suffer from low accuracy and performance fluctuation when the distributions of old and new data are significantly different. |
| Approach: | They propose a hyperparameter-free continual learning model for text data that can stably produce high performance under various environments. |
| Outcome: | The proposed model outperforms the best state-of-the-art method by 20% in average accuracy and each component contributes effectively to overall performance. |
KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)
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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu
| Challenge: | Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos. |
| Approach: | They propose a framework that decouples epistemic planning from content generation. |
| Outcome: | The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage. |
CRUISE: Cold-Start New Skill Development via Iterative Utterance Generation (P18-4)
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| Challenge: | Existing systems require developers to manually generate and annotate a large number of utterances. |
| Approach: | They propose a system that guides ordinary software developers to build a high quality NLU engine from scratch. |
| Outcome: | The proposed system shows that iterative pruning of incorrect utterances reduces human workload and cognitive load. |
SLiM: Speculative Decoding with Hypothesis Reduction (2024.findings-naacl)
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| Challenge: | Speculative decoding has emerged as an alternative to autoregressive decoding for expediting inference in large language models (LLMs). prevailing assumptions focus solely on latency reduction, neglecting the computational expenses. |
| Approach: | They propose a speculative decoding enhancement to reduce the speculation set while validating more effective tokens. |
| Outcome: | The proposed method reduces the speculation set while validating more effective tokens. |
Dynamic Low-rank Estimation for Transformer-based Language Models (2023.findings-emnlp)
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| Challenge: | RankDyna is a matrix decomposition method that can be used to compress Transformer-based language models. |
| Approach: | They propose a matrix decomposition method that enables dynamic rank resource allocation . they say it can outperform current SOTA methods under various parameter budget levels . |
| Outcome: | The proposed method outperforms current SOTA methods under various budget levels . the proposed method is more efficient with higher compression rates . |
A Progressive Model to Enable Continual Learning for Semantic Slot Filling (D19-1)
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| Challenge: | Existing approaches to slot filling training on large scale data are inefficient and require multiple trainings. |
| Approach: | They propose a slot filling model that transfers previously learned knowledge to a small size expanded component and enables it to be fast trained to learn from new data. |
| Outcome: | The proposed model outperforms existing models on two benchmark datasets by 4.24% and 3.03% on the same dataset. |
Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU (2021.acl-long)
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| Challenge: | Existing methods for intent classification are expensive to collect and train . evaluators have shown that the ability to detect out-of-domain utterances is limited . |
| Approach: | They propose to train a model with only IND data while supporting both intent classification and OOD detection. |
| Outcome: | The proposed model improves on existing models and strong baselines on four datasets. |
SkillBot: Towards Automatic Skill Development via User Demonstration (N19-4)
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| Challenge: | Existing industrial PA products require software developers to build new skills via IDE tools. |
| Approach: | They propose a software that automatically develops a natural language understanding engine and implements the action without the need of coding. |
| Outcome: | The proposed system performs well on both benchmark and in-house datasets. |