Papers by Tom Goldstein
LLM-Generated Passphrases That Are Secure and Easy to Remember (2025.findings-naacl)
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| Challenge: | Automated password generation is a cornerstone of IT security, but passphrases are often hard to remember and see limited adoption. |
| Approach: | They propose to use large language models to generate passphrases with sufficient entropy . they also use prompt construction to generate a grammatical model with sufficient output entropic . |
| Outcome: | The proposed methods show that the outputs are highly reliable and easy to remember . the proposed methods also show that prompt construction is important in steering the output topic and grammatical structure. |
Confidence Improves Self-Consistency in LLMs (2025.findings-acl)
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| Challenge: | Modern large language models (LLMs) demonstrate strong reasoning capabilities, driven in part by their capacity to generate a sequence of intermediate reasoning steps that lead them toward a final answer. |
| Approach: | They propose a method that performs a weighted majority vote based on confidence scores obtained directly from the model. |
| Outcome: | The proposed method outperforms self-consistency on nine models and four datasets, reducing the required number of reasoning paths by over 40% on average. |
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. |