Papers by Yi Liao
Mitigating Contradictions in Dialogue Based on Contrastive Learning (2022.findings-acl)
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| Challenge: | Current chatbots generate fluent, informative responses but sometimes generate contradictory responses when interacting with human. |
| Approach: | They propose to use contrastive learning technique to mitigate contradiction issues in chatbots by minimizing the similarity between the target response and contradiction related negative example. |
| Outcome: | The proposed method outperforms existing methods on automatic and human evaluation while preserving response fluency. |
Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order (2020.acl-main)
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| Challenge: | Large-scale pretrained language models such as masked language model (MLM) have brought significant improvements to many NLU and NLG tasks. |
| Approach: | They propose a probabilistic masking scheme for the masked language model and a model with a uniform prior distribution on the masking ratio. |
| Outcome: | The proposed model outperforms BERT on a bunch of downstream NLG tasks. |
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models (2021.naacl-main)
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| Challenge: | Existing methods to accelerate inference speed of pre-trained language models are limited to local representations of exit layer . current models are associated with large memory requirement and high computational cost, which slow down inference and further encumber the application of PLMs. |
| Approach: | They propose a method to exit early without passing through all inference layers . they take into consideration all the linguistic information embedded in the past layers a global perspective . |
| Outcome: | The proposed method outperforms existing methods by a large margin . it uses linguistic information embedded in the past layers and future features . the proposed method is scalable and cost-effective . |
NL ⇒ Schedule: Evaluate Multitask Scheduling Capability of Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for scheduling from natural language descriptions rely on experts with limited scheduling skills and domain knowledge. |
| Approach: | They propose a model to generate a feasible schedule from natural language descriptions. |
| Outcome: | The proposed framework achieves more robust performance than six state-of-the-art LLM+solver methods. |
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)
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| Challenge: | Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. |
| Approach: | They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. |
| Outcome: | The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%. |
TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models (2021.acl-long)
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| Challenge: | Using pretrained language models, we propose an error-annotated dataset for text generation . we use carefully selected prompt words to guide GPT-2 to generate candidate sentences . |
| Approach: | They propose an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models. |
| Outcome: | The proposed dataset covers 24 types of errors according to common sense and linguistics. |
Exploring and Distilling Multi-Dimensional Clues for Interpretable Social Bot Detection (2026.acl-long)
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| Challenge: | Existing research on social bot detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy. |
| Approach: | They propose a four-dimensional clue framework that uses outcome-reward reinforcement learning to train inspectors to generate faithful, grounded clues from user information, semantic features, interactive situation, and behavioral pattern. |
| Outcome: | The proposed framework outperforms baselines in detection performance and significantly improves the performance of large language models. |
QuaSE: Sequence Editing under Quantifiable Guidance (D18-1)
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| Challenge: | Existing methods for Quantifiable Sequence Editing (QuaSE) require editing an input sequence to generate an output that satisfies a numerical outcome value measuring a certain property of the sequence. |
| Approach: | They propose a framework for Quantifiable Sequence Editing that allows editing an input sequence to change an outcome and keep the content. |
| Outcome: | The proposed framework disentangles outcome factor and content factor from the input sentence to allow editing to change the outcome and keep the content. |