Papers by Yubin Ge
StereoMap: Quantifying the Awareness of Human-like Stereotypes in Large Language Models (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have been observed to encode harmful associations present in the training data. |
| Approach: | They propose a framework to map LLMs' perceptions of how demographic groups have been viewed by society using the dimensions of Warmth and Competence. |
| Outcome: | The proposed framework maps LLMs’ perceptions of social groups using the dimensions of Warmth and Competence. |
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis (P19-1)
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| Challenge: | Experimental results show that our proposed approach yields better attention mechanisms . dominant ASC models are mostly discriminative classifiers based on manual feature engineering . |
| Approach: | They propose a self-supervised approach to aspect-level sentiment classification that mines useful attention supervision information from a training corpus to refine attention mechanisms. |
| Outcome: | The proposed approach yields better attention mechanisms on multiple datasets. |
Supplement Generation Training for Enhancing Agentic Task Performance (2026.findings-acl)
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Young Min Cho, Daniele Bonadiman, Divya Bhargavi, Tamer Alkhouli, Salvatore Romeo, Dongwei Jiang, Khushbu Pahwa, Yubin Ge, Etsuko Ishii, Monica Sunkara, Yi Zhang
| Challenge: | Training large foundation models for agentic tasks is impractical due to high computational costs, long iteration cycles, and rapid obsolescence as new models are released. |
| Approach: | They propose a method that trains a small LLM to generate supplemental text that helps the larger LLM solve the task more effectively. |
| Outcome: | The proposed approach decouples task-specific optimization from large foundation models . it achieves consistent and significant performance gains across diverse tasks and models - all without gradient access to the actor model. |
TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues (2025.findings-acl)
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| Challenge: | Temporal reasoning in multi-session dialogues presents a significant challenge which has been under-studied in previous temporal reasoning benchmarks. |
| Approach: | They propose to augment LoCoMo dialogues and create multi-choice QAs to construct a temporal reasoning evaluation task and a framework to enhance temporal thinking capabilities of LLM-agents. |
| Outcome: | The proposed framework significantly improves temporal reasoning performance compared to baseline methods, raising from 29.83 on GPT-4o via standard prompting to 77.67 via the proposed framework. |
Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)
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| Challenge: | Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation. |
| Approach: | They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model. |
| Outcome: | Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training . |
BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation (2021.acl-long)
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| Challenge: | citing sentences capture salient information in cited papers and the connection between citing and citing papers. |
| Approach: | They propose a BAckground knowledge- and COntent-based framework for citing sentence generation that integrates two types of information: background knowledge and content. |
| Outcome: | The proposed framework outperforms baselines in the citation sentence generation task. |
Detection and Mitigation of the Negative Impact of Dataset Extractivity on Abstractive Summarization (2023.findings-acl)
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| Challenge: | Existing studies have shown that extractivity can affect output extractivity and the amount of factual information (i.e. faithfulness) in abstractive summarization models. |
| Approach: | They propose to design copy labels to fix the model's copying behaviors and train the model with a copy mechanism to reduce the negative impact of high extractivity on model performance. |
| Outcome: | The proposed method outperforms several competitive baselines and shows that low extractivity can improve model performance, while higher extractivity leads to a tendency for the model to copy text continuously from the source document rather than identifying and summarizing important content. |
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings (2021.emnlp-main)
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Shaopeng Lai, Ante Wang, Fandong Meng, Jie Zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, Jinsong Su
| Challenge: | Existing sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. |
| Approach: | They propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering. |
| Outcome: | The proposed model achieves state-of-the-art performance on five commonly-used datasets. |
SAMULE: Self-Learning Agents Enhanced by Multi-level Reflection (2025.emnlp-main)
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| Challenge: | Modern AI agents rely on Large Language Models (LLMs) as their reasoning engines, but they still face the challenge of generating meaningful reflections due to inadequate error analysis and a reliance on rare successful trajectories. |
| Approach: | They propose a framework for self-learning agents powered by a retrospective language model that generates reflections during inference. |
| Outcome: | The proposed framework outperforms reflection-based baselines on three challenging benchmarks. |