40 citations · 52 across the 9 of their papers we have counts for
10 papers
Jailbreak Distillation: Renewable Safety Benchmarking
Jingyu Zhang, Ahmed Elgohary, Xiawei Wang +5
Large language models (LLMs) are rapidly deployed in critical applications, raising urgent needs for robust safety benchmarking. We propose Jailbreak Distillation (JBDistill), a no…
Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements
Jingyu Zhang, Ahmed Elgohary, Ahmed Magooda +2
The current paradigm for safety alignment of large language models (LLMs) follows a one-size-fits-all approach: the model refuses to interact with any content deemed unsafe by the…
Persuasiveness of Generated Free-Text Rationales in Subjective Decisions: A Case Study on Pairwise Argument Ranking
Mohamed Elaraby, Diane Litman, Xiang Lorraine Li +1
Generating free-text rationales is among the emergent capabilities of Large Language Models (LLMs). These rationales have been found to enhance LLM performance across various NLP t…
LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localization
Akshita Gupta, Gaurav Mittal, Ahmed Magooda +3
Temporal Action Localization (TAL) involves localizing and classifying action snippets in an untrimmed video. The emergence of large video foundation models has led RGB-only video…
A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications
Ahmed Magooda, Alec Helyar, Kyle Jackson +14
We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automa…
Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization
Ahmed Magooda, Diane Litman
This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any addition…
Exploring Multitask Learning for Low-Resource AbstractiveSummarization
Ahmed Magooda, Mohamed Elaraby, Diane Litman
This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different task…
Abstractive Summarization for Low Resource Data using Domain Transfer and Data Synthesis
Ahmed Magooda, Diane Litman
Training abstractive summarization models typically requires large amounts of data, which can be a limitation for many domains. In this paper we explore using domain transfer and d…
Attend to the beginning: A study on using bidirectional attention for extractive summarization
Ahmed Magooda, Cezary Marcjan
Forum discussion data differ in both structure and properties from generic form of textual data such as news. Henceforth, summarization techniques should, in turn, make use of such…
eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing
Haoran Zhang, Ahmed Magooda, Diane Litman +5
Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that use…