Papers by Yiduo Guo
Large Language Models Can Learn Representation in Natural Language (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are unable to complete complex tasks involving multiple entities, such as tool APIs. |
| Approach: | They propose a method which uses natural language representations to refine entity descriptions for improved retrieval and LLM utilization. |
| Outcome: | The proposed method improves GPT-4's performance on classification tasks and API call tasks. |
English as Defense Proxy: Mitigating Multilingual Jailbreak via Eliciting English Safety Knowledge (2025.findings-emnlp)
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| Challenge: | Large language models excel in many tasks, but their safety guarantees vary by language. |
| Approach: | They propose a unified approach that leverages English as a universal safety anchor. |
| Outcome: | The proposed approach leverages English as defense proxy (E-Proxy) to transfer safety knowledge across languages. |
Learning to Plan by Updating Natural Language (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance in basic natural language tasks. |
| Approach: | They propose a method that iteratively updates the task plan with new steps and behavioral instructions to guide LLMs to generate the correct solutions step by step. |
| Outcome: | The proposed method improves performance on five reasoning type tasks and can be directly applied to other LLMs. |
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast (2023.acl-long)
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| Challenge: | Existing research shows that a multilingual pre-trained language model fine-tuned with one (source) language performs well on downstream tasks for non-source languages . However, there is a clear performance gap between the source and non-sourced languages - this gap can be reduced by reducing forgetting. |
| Approach: | They propose a method to fine-tune a multilingual pre-trained language model fine- tuned with one (source) language and four training policies to address the performance gap. |
| Outcome: | The proposed method outperforms baselines on the XNLI dataset by a clear margin. |
PPTC-R benchmark: Towards Evaluating the Robustness of Large Language Models for PowerPoint Task Completion (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly used for task completion in real-world situations. |
| Approach: | They propose a PowerPoint Task Completion-Robustness (PPTC-R) benchmark to measure LLMs’ robustness to the user PPT task instruction and software version (Powerpoint). |
| Outcome: | The proposed benchmark compares 3 closed-source and 4 open-source LLMs to the PowerPoint task instruction and software version (Powerpoint) . |
PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion (2024.findings-acl)
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| Challenge: | Recent evaluations of Large Language Models (LLMs) focus on their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs. |
| Approach: | They propose a PowerPoint Task Completion benchmark to assess LLMs’ ability to create and edit PPT files based on user instructions. |
| Outcome: | The proposed system outperforms open-source and closed LLMs with 75.1% accuracy in single-turn dialogue testing but only achieves 6% session accuracy. |
Class-Incremental Learning based on Label Generation (2023.acl-short)
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| Challenge: | Existing studies on pre-trained language models focus on task-incremental learning (TIL) but they perform poorly in a more challenging setting of class-incremental learning. |
| Approach: | They propose a method which solves CIL based on label generation by using sparse vocabulary and creates pseudo-replay samples by using label semantics. |
| Outcome: | The proposed method outperforms baseline models by a large margin in the class-incremental learning setting. |
Efficient Domain Continual pretraining by Mitigating the Stability Gap (2025.acl-long)
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| Challenge: | Continual pretraining is an important approach for Large Language Models to improve their performance in target domains, learn new topics and languages, and even boost their general capabilities. |
| Approach: | They propose a training strategy that mitigates instability by increasing the number of epochs, along with two data sampling strategies targeting data domain relevance and corpus distribution. |
| Outcome: | The proposed training strategy improves the average medical task performance of the OpenLlama-3B model from 36.2% to 40.7% using only 40% of the original training budget, while also enhancing general task performance without causing forgetting. |
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models (2024.findings-naacl)
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Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan
| Challenge: | Traditional benchmarks for evaluating foundation models often fail to accurately represent their general abilities for human-centric tasks. |
| Approach: | They propose a bilingual benchmark to assess foundation models in the context of human-centric standardized exams such as college entrance exams, law school admission tests, and math competitions. |
| Outcome: | The proposed benchmark exceeds the average human performance on SAT, LSAT, and math competitions with 95% accuracy and 92.5% on the Chinese college entrance English exam. |