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Found 5 papers, 2 papers with code
Gender Biased Legal Case Retrieval System on Users' Decision Process
Gender of the defendants in both the task and relevant cases was edited to statistically measure the effect of gender bias in the legal case search results on participants' perceptions.
SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization
Large Language Models (LLMs) such as GPT & Llama have demonstrated significant achievements in summarization tasks but struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences.
Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation
We recommend a two-phase optimization process, leveraging APO-GPT4 for consistency and expert input for personalization.
Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization
In this work, we propose a new pipeline using ChatGPT instead of human experts to generate high-quality feedback data for improving factual consistency in the clinical note summarization task.
Investigating the Influence of Legal Case Retrieval Systems on Users' Decision Process
Given a specific query case, legal case retrieval systems aim to retrieve a set of case documents relevant to the case at hand.
