Papers by Daniel Katz

3 papers
Answering Questions by Meta-Reasoning over Multiple Chains of Thought (2023.emnlp-main)

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Challenge: Modern systems for multi-hop question answering (QA) break questions into a sequence of reasoning steps, termed chain-of-thought (CoT) Often, multiple chains are sampled and aggregated, but the intermediate steps themselves are discarded.
Approach: They propose a method which prompts large language models to meta-reason over multiple chains of thought rather than aggregate their answers.
Outcome: The proposed approach outperforms baselines on 7 multi-hop QA datasets.
LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development (2023.acl-long)

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Challenge: In this study, we examine the performance of legal-oriented pre-trained language models.
Approach: They conduct a detailed analysis on the performance of legal-oriented pre-trained language models by examining their original objective, acquired knowledge, and legal language understanding capacities.
Outcome: The results show that the models' size and pre-training corpora are important for the development of domain-specific models.
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English (2022.acl-long)

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Challenge: Laws and their interpretations, legal arguments and agreements are typically expressed in writing.
Approach: They propose a benchmark to evaluate model performance across legal NLU tasks . they also evaluate several generic and legal-oriented models .
Outcome: The proposed model performs better across multiple tasks than previous models.

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