Papers by Liwen Hou

2 papers
Detecting Syntactic Change with Pre-trained Transformer Models (2023.findings-emnlp)

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Challenge: a fine-tuned BERT model can distinguish between text from the early 1800s and late 1900s . we use it to identify specific instances of syntactic change and specific words for which a new part of speech was introduced.
Approach: They propose to use a BERT-based model to find syntactic differences between English of the early 1800s and that of the late 1900s.
Outcome: The proposed model can distinguish between English of the early 1800s and that of the late 1900s using only syntactic information.
FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios (2026.findings-acl)

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Challenge: Existing large language models (LLMs) are prone to misuse and misinformation, posing serious compliance risks.
Approach: They propose a bilingual red-teaming benchmark to test an LLM’s refusal of requests that violate financial compliance.
Outcome: The proposed benchmark is based on real-world financial crime cases and ethical violations and includes 14 subcategories covering financial crimes and ethical breaches.

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