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Found 10 papers, 3 papers with code
Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment
This work introduces the first framework for reconstructing surgical dialogue from unstructured real-world recordings, which is crucial for characterizing teaching tasks.
Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment
Creating such an automated system poses challenges, as it requires an understanding of both the verbal feedback delivered by the trainer and the visual context of the real-time surgical scene.
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs
Our results reveal that: (1) ChatGPT can efficiently produce high-quality training data for debiasing other LLMs; (2) data produced via our approach surpasses existing datasets in debiasing performance while also preserving internal knowledge of a pre-trained LLM; and (3) synthetic data exhibits generalizability across categories, effectively mitigating various biases, including intersectional ones.
Deep Multimodal Fusion for Surgical Feedback Classification
This work offers an important first look at the feasibility of automated classification of real-world live surgical feedback based on text, audio, and video modalities.
Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models
Text-to-image diffusion models have been adopted into key commercial workflows, such as art generation and image editing.
BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Models
We thus enable seamless open-ended social bias testing of PLMs by domain experts through an automatic large-scale generation of diverse test sentences for any combination of social categories and attributes.
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions
Finally, we find that not all transfer scenarios yield a positive gain, which seems related to the PLMs initial performance on the target-domain task.
From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks
To improve the discovery experience we introduce multiple new methods for \em augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user's publication and interaction history.
Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases
We then provide the LM with instruction that consists of this subset of labeled exemplars, the query text to be classified, a definition of bias, and prompt it to make a decision.
Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop
Although information workers may complain about meetings, they are an essential part of their work life.
