12 citations · 19 across the 6 of their papers we have counts for
6 papers
Analyzing the Performance of Large Language Models on Code Summarization
Rajarshi Haldar, Julia Hockenmaier
Large language models (LLMs) such as Llama 2 perform very well on tasks that involve both natural language and source code, particularly code summarization and code generation. We…
ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied to Vision Transformers
Ikhyun Cho, Changyeon Park, Julia Hockenmaier
Machine unlearning (MUL) is an arising field in machine learning that seeks to erase the learned information of specific training data points from a trained model. Despite the rece…
Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization
Yoonhwa Jung, Ikhyun Cho, Shun-Hsiang Hsu +1
With growing concerns surrounding privacy and regulatory compliance, the concept of machine unlearning has gained prominence, aiming to selectively forget or erase specific learned…
Human-guided Collaborative Problem Solving: A Natural Language based Framework
Harsha Kokel, Mayukh Das, Rakibul Islam +10
We consider the problem of human-machine collaborative problem solving as a planning task coupled with natural language communication. Our framework consists of three components --…
Evaluating Induced CCG Parsers on Grounded Semantic Parsing
Yonatan Bisk, Siva Reddy, John Blitzer +2
We compare the effectiveness of four different syntactic CCG parsers for a semantic slot-filling task to explore how much syntactic supervision is required for downstream semantic…
Reasoning about RoboCup Soccer Narratives
Hannaneh Hajishirzi, Julia Hockenmaier, Erik T. Mueller +1
This paper presents an approach for learning to translate simple narratives, i.e., texts (sequences of sentences) describing dynamic systems, into coherent sequences of events with…