Papers by Rohan Bhambhoria

5 papers
Prototype-Based Interpretability for Legal Citation Prediction (2023.findings-acl)

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Challenge: citation prediction is a key problem in high-stakes decision making areas such as law . experts often require interpretability for automatic systems to be utilized in practical settings .
Approach: They propose to use legal citation prediction to solve a problem with legal experts' feedback . they propose to add a prototype architecture to add interpretability while adhering to legal parameters .
Outcome: The proposed model performs well while adhering to decision parameters used by lawyers.
A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification (2023.acl-short)

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Challenge: Large language models have been able to perform well on benchmarks, but they are often not able address real-world challenges.
Approach: They propose to refactor hierarchical tasks into a more indicative long-tail prediction task.
Outcome: The proposed method does not require parameter updates and achieves strong performance across multiple datasets.
Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation (2024.findings-emnlp)

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Challenge: Existing research has focused on the veracity of information, overlooking the legal implications and consequences of misinformation.
Approach: They propose a task to detect misinformation using legal issues as a measure of societal ramifications.
Outcome: The proposed task leverages definitions from a wide range of legal domains covering 4 broader legal topics and 11 fine-grained legal issues, including hate speech, election laws, and privacy regulations.
Legally Enforceable Hate Speech Detection for Public Forums (2023.findings-emnlp)

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Challenge: Existing work does not align systems with enforceable hate speech definitions, which can make outputs inconsistent with the goals of regulators.
Approach: They propose a task for enforceable hate speech detection centred around legal definitions and an annotated dataset of violations by legal experts.
Outcome: The proposed method can be used to detect hate speech in public forums on a large scale.
Prefix Propagation: Parameter-Efficient Tuning for Long Sequences (2023.acl-short)

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Challenge: Prefix-tuning prepends trainable tokens to sequences while freezing the rest of the model’s parameters.
Approach: They propose a method that prefixes on previous hidden states to improve model performance.
Outcome: The proposed architecture outperforms prefix-tuning on long-document tasks while using 50% fewer parameters.

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