Papers by Lili Wang
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing (2022.naacl-main)
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| Challenge: | Document-level relation extraction models are not robust and exhibit bizarre behaviors when non-evidence sentences are removed. |
| Approach: | They propose a document-level relation extraction framework that uses a sentence importance score and a focusing loss to encourage DocRE models to focus on evidence sentences. |
| Outcome: | The proposed framework improves overall performance and makes DocRE models more robust. |
Intersectional Stereotypes in Large Language Models: Dataset and Analysis (2023.findings-emnlp)
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| Challenge: | Existing studies on intersectional stereotypes focus on broader, individual categories . current studies focus on single-group stereotypes, such as racial bias against African Americans . |
| Approach: | They propose to use a dataset of intersectional stereotypes curated with the ChatGPT model to analyze propagation in three contemporary LLMs. |
| Outcome: | The proposed dataset enables analysis of stereotype propagation in three contemporary LLMs. |
Time-for-Accuracy: Formalizing Chain-of-Thought as an Expansion of Logical Depth (2026.findings-acl)
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| Challenge: | Chain-of-thought (CoT) prompting can improve multi-step reasoning, but it is unclear what kind of additional sequential computation longer traces actually enable. |
| Approach: | They propose a deletion-based measure of step necessity under a specified inference interface to operationalize realized depth beyond raw length. |
| Outcome: | The proposed method combines effective logical depth with Bennett's logical depth to show that it is more efficient than a linear model. |
NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models (2025.findings-acl)
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Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu, Yu Lu, Yaochu Jin, Lili Pan, Zhenzhong Lan
| Challenge: | Existing approaches to generate research ideas rely on retrieval or prompt engineering to generate ideas. |
| Approach: | They propose a method that uses iterative planning and search to boost creative potential of LLMs by integrating external knowledge with broader and deeper insights. |
| Outcome: | The proposed method outperforms the current state-of-the-art in generating 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation. |
Deciphering Stereotypes in Pre-Trained Language Models (2023.emnlp-main)
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Weicheng Ma, Henry Scheible, Brian Wang, Goutham Veeramachaneni, Pratim Chowdhary, Alan Sun, Andrew Koulogeorge, Lili Wang, Diyi Yang, Soroush Vosoughi
| Challenge: | Current approaches for examining stereotypes in PLMs require intricate human knowledge about these stereotypes and entail careful manual curation of examples. |
| Approach: | They propose a framework for examining stereotype-encoding behavior of PLMs using model probing and textual analyses. |
| Outcome: | The proposed approach can debiase PLMs without compromising their language modeling capabilities or performance. |
LeanK: Learnable K Cache Channel Pruning for Efficient Decoding (2025.emnlp-main)
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| Challenge: | Existing efforts to optimize the key-value (KV) cache include: (1) Eviction, which discards cache of less important tokens; (2) Selection, which retains the full KV cache but selectively reads relevant entries. |
| Approach: | They propose a learning-based method that prunes unimportant key (K) cache channels by leveraging static channel sparsity. |
| Outcome: | Experiments show that LeanK reduces GPU memory and accelerates decoding without sacrificing accuracy. |
Double-Checker: Large Language Model as a Checker for Few-shot Named Entity Recognition (2024.findings-emnlp)
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| Challenge: | Recent studies have demonstrated remarkable performance on few-shot Named Entity Recognition tasks due to the high cost of obtaining high-quality labeled data. |
| Approach: | They propose to decompose the task into entity span detection and entity type classification using a type-independent entity span detector and then classify the detected spans based on their types. |
| Outcome: | The proposed method consistently yields improvements over two baseline approaches. |
Interactive Classification by Asking Informative Questions (2020.acl-main)
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| Challenge: | Existing methods for intent classification rely on a single user input and do not interact with the user to reduce ambiguity and improve the final prediction. |
| Approach: | They propose a limited form of interaction to natural language intent classification . they add binary or multi-choice questions to the system to ask missing information . |
| Outcome: | The proposed method can be bootstrapped without interaction data and is scalable to two domains. |
Simulated Misinformation Susceptibility (SMISTS): Enhancing Misinformation Research with Large Language Model Simulations (2024.findings-acl)
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| Challenge: | Psychological inoculations have shown efficacy in curbing its spread and mitigating its adverse effects at early stages, but their design and optimization typically requires substantial human and financial resources due to the need for repeated experimental trials. |
| Approach: | They propose to use large language models to simulate participant responses in misinformation studies to mitigate caricatures and stereotypes in the simulations. |
| Outcome: | The proposed method mitigates caricatures and stereotypes in LLM simulations and enhances response diversity. |
Position Engineering: Boosting Large Language Models through Positional Information Manipulation (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated significant strides towards achieving artificial general intelligence. |
| Approach: | They propose a technique termed position engineering which alters the positional information in the prompt without modifying the text itself. |
| Outcome: | The proposed technique significantly improves on the baseline in retrieval-augmented generation and in-context learning scenarios. |
Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks (2021.acl-long)
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| Challenge: | Prior research has found that only a few attention heads are important in each mono-lingual NLP task and pruning the remaining heads leads to comparable or improved performance of the model. |
| Approach: | They examine the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks. |
| Outcome: | The proposed model performs better with the remaining heads pruned than with the other models, the authors show . |
EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English (2022.findings-acl)
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| Challenge: | Existing research on cultural background modeling is coarse-grained and does not examine cultural differences among speakers of the same language. |
| Approach: | They use a news-based cultural background prediction dataset to annotate, validate and benchmark NLP models with cultural background features. |
| Outcome: | The proposed model improves on nine syntactic, semantic, and psycholinguistic tasks while introducing cultural background information does not improve the Go-Emotions task due to text domain conflicts. |
Multi-resolution Annotations for Emoji Prediction (2020.emnlp-main)
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| Challenge: | Emojis are able to express various linguistic components, such as emotions, sentiments, events, etc. emojis have the merit of preserving information more densely, compared to words, argues a new study. |
| Approach: | They propose to use passage-level and aspect-level emoji annotations to predict the proper emmojis associated with text. |
| Outcome: | The proposed method is heuristically generated and validated with a pre-trained BERT model. |
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer (D19-1)
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| Challenge: | Existing studies normalize informal sentences with rules, but they introduce noise if we use them in a naive way. |
| Approach: | They propose to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. |
| Outcome: | The proposed method can be used to generate a state-of-the-art on a small dataset. |
Formality Style Transfer with Shared Latent Space (2020.coling-main)
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| Challenge: | Existing approaches for formality style transfer use neural networks for sentence generation, but the dataset for formal style transfer is considerably smaller than translation corpora. |
| Approach: | They propose a new approach for formality style transfer using shared latent space and two auxiliary losses. |
| Outcome: | The proposed approach outperforms baselines in various settings, especially when limited data is available. |
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation (2020.emnlp-main)
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| Challenge: | Existing methods for data augmentation produce low readability or semantic consistency. |
| Approach: | They propose a framework which augments data through reinforcement learning guided conditional generation. |
| Outcome: | The proposed framework improves F1 performance on three different classification tasks by 8.7% on average when given only 10% of the whole data for training. |
LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics (2025.coling-main)
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| Challenge: | Emotion recognition in conversation (ERC) is a task of discerning human emotions for each utterance within a conversation. |
| Approach: | They propose a framework that uses large language models to analyze speaker characteristics . they use two-stage learning to make the models reason speaker characteristics and track emotion of the speaker . |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks (2021.emnlp-main)
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| Challenge: | A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically. |
| Approach: | They propose an automatic auxiliary task selection method based on gradient calculation in Transformer-based models that improves MT-DNN performance. |
| Outcome: | The proposed method improves MT-DNN performance on 8 natural language understanding (GLUE) tasks, while costing less than AUTOSEM and comparable GPU consumption. |
Improving Syntactic Probing Correctness and Robustness with Control Tasks (2023.acl-short)
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Weicheng Ma, Brian Wang, Hefan Zhang, Lili Wang, Rolando Coto-Solano, Saeed Hassanpour, Soroush Vosoughi
| Challenge: | Syntactic probing methods are biased by the PLMs’ memorization of common word co-occurrences, even if they do not form syntactical relations. |
| Approach: | They propose to use random word substitution and random label matching to reduce these biases and improve the robustness of syntactic probing methods. |
| Outcome: | The proposed tasks improve probing results and consistency between probing methods and make them more generalizable to unseen text domains. |