Papers by Ximing Liu
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts (2021.acl-long)
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Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, Yejin Choi
| Challenge: | Decoding-time Experts is a decoding- time method for controlled text generation . it combines a pretrained language model with "expert" LMs and/or "anti-expert" experts . |
| Approach: | They propose a decoding-time method that combines a pretrained language model with "expert" LMs and/or "anti-expert" experts to generate controlled text. |
| Outcome: | The proposed method outperforms existing controllable generation methods on automatic and human evaluations. |
Generated Knowledge Prompting for Commonsense Reasoning (2022.acl-long)
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Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
| Challenge: | Existing methods for commonsense reasoning rely on high-quality knowledge, but they are often dominated by large-scale pretrained models that are fine-tuned on a target benchmark. |
| Approach: | They develop generated knowledge prompting which generates knowledge from a language model and provides it as additional input when answering a question. |
| Outcome: | The proposed method improves state-of-the-art models on four commonsense reasoning tasks. |
On the Step Length Confounding in LLM Reasoning Data Selection (2026.findings-acl)
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Bing Wang, Rui Miao, Chen Shen, Shaotian Yan, Kaiyuan Liu, Ximing Li, Xiaosong Yuan, Sinan Fan, Jun Zhang, Jieping Ye
| Challenge: | Existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Approach: | They propose to use supervised fine-tuning to generate long reasoning data from more capable Large Language Models and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Outcome: | Experiments on four LLMs and five evaluation benchmarks show that the proposed approach is effective in mitigating step length confounding problem. |
metaCAT: A Metadata-based Task-oriented Chatbot Annotation Tool (2020.aacl-demo)
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| Challenge: | Creating high-quality annotated dialogue corpora necessitates a high level of human engagements. |
| Approach: | They propose to develop an annotation tool specifically for developing task-oriented dialogue data that provides comprehensive metadata annotation coverage to the domain, intent, and span information. |
| Outcome: | The tool provides comprehensive metadata annotation coverage to domain, intent, and span information. |
Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering (2022.emnlp-main)
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| Challenge: | Recent research shows that relevant knowledge can provide useful context for commonsense tasks. |
| Approach: | They propose a method that learns to generate contextually relevant knowledge in response to given questions. |
| Outcome: | The proposed method shows consistent gains over 9 commonsense benchmarks. |
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning (2021.findings-acl)
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| Challenge: | Existing approaches to multi-task learning fail to capture label correlations . Existing methods suffer from label order dependency, label combination over-fitting and error propagation problems. |
| Approach: | They propose a novel approach with multi-task learning to enhance label correlation feedback. |
| Outcome: | The proposed method outperforms baselines on AAPD and RCV1-V2 datasets. |