Papers by Jiuhai Chen
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements (2024.findings-acl)
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| Challenge: | Existing LLMs lack sufficient controllability to generate statements supporting diverse or even controversial perspectives. |
| Approach: | They develop a pipeline that fine tunes LLMs to generate statements generated via debate. |
| Outcome: | The proposed pipeline improves the controllability of LLMs in generating statements supporting an argument the user defined in the prompt. |
How Many Demonstrations Do You Need for In-context Learning? (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) are capable of complex reasoning when given a few input-output demos. |
| Approach: | They use fewer input-output demos for each test query to study ICL . they do not observe significant degradation when using only one randomly chosen demo . |
| Outcome: | The proposed model outperforms multi-demo models on the tasks in 2022. |
Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness (2024.acl-long)
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| Challenge: | BSDetector is a method for detecting bad and speculative answers from a pretrained Large Language Model. |
| Approach: | They propose a method for detecting bad and speculative answers from a pretrained Large Language Model by estimating a confidence score for any output it generated. |
| Outcome: | Experiments on open-form Question-Answer benchmarks show that BSDetector more accurately identifies incorrect LLM responses than alternative uncertainty estimation procedures. |
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning (2024.naacl-long)
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Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao
| Challenge: | Large Language Models (LLMs) have revolutionized the landscape of artificial intelligence. |
| Approach: | They propose a self-guided method to identify and select cherry samples from open-source datasets, minimizing manual curation and potential cost for instruction tuning an LLM. |
| Outcome: | The proposed method enables LLMs to identify discrepancies between expected responses and intrinsic generation capability, and a marked uptick in model training efficiency. |
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning (2024.findings-acl)
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| Challenge: | Instruction tuning is critical to large language models but its success heavily relies on the training data quality. |
| Approach: | They propose a paradigm that synergizes a teacher LLM’s reflection and introspection with the data selection capability of the student LLM to automatically refine existing instruction-tuning data. |
| Outcome: | The proposed method achieves much stronger and top-tier 7B and 13B LLMs without collecting brand-new data. |
Multi-Objective Linguistic Control of Large Language Models (2024.findings-acl)
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| Challenge: | Existing Large language models prefer to generate verbose responses due to the length bias, which may increase unnecessary reading complexity. |
| Approach: | They propose to use off-the-shelf data to fine tune multiple linguistic complexities of LLM outputs to improve multi-complexity controllability and improve the quality of the responses. |
| Outcome: | The proposed method improves multi-complexity controllability significantly and retains or enhances the quality of the responses as a side benefit. |
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement (2025.findings-naacl)
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Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou, Yiyang Zhou, Huaxiu Yao, Tianyi Zhou, Tom Goldstein, Parminder Bhatia, Taha Kass-Hout, Furong Huang, Cao Xiao
| Challenge: | Existing methods for visual and language alignment depend on external models or data, leading to uncontrollable and unstable results. |
| Approach: | They propose a framework that enhances visual and language alignment without external dependencies by incorporating an in-context self-critic mechanism that constructs preference pairs for tuning. |
| Outcome: | The proposed framework outperforms existing methods and improves performance on 14 hallucination and comprehensive benchmarks. |
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer (2023.emnlp-main)
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| Challenge: | Existing prompt tuning methods have training instability issues due to large variance of scores . existing prompt tuning algorithms have training stability issues due a slight change of input data . |
| Approach: | They propose an algorithm that smooths the loss landscape of vanilla prompt tuning by perturbation-based regularizers. |
| Outcome: | The proposed method improves the state-of-the-art prompt tuning methods by 1.94% and 2.34% on SuperGLUE and FewGLUE benchmarks. |