14 citations · 102 across the 23 of their papers we have counts for
6 papers · 1 filter
Towards Understanding What Code Language Models Learned
Toufique Ahmed, Dian Yu, Chengxuan Huang +3
Pre-trained language models are effective in a variety of natural language tasks, but it has been argued their capabilities fall short of fully learning meaning or understanding la…
Better patching using LLM prompting, via Self-Consistency
Toufique Ahmed, Premkumar Devanbu
Large Language models (LLMs) can be induced to solve non-trivial problems with "few-shot" prompts including illustrative problem-solution examples. Now if the few-shots also includ…
A Survey of Trojans in Neural Models of Source Code: Taxonomy and Techniques
Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed +4
In this work, we study literature in Explainable AI and Safe AI to understand poisoning of neural models of code. In order to do so, we first establish a novel taxonomy for Trojan…
Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)
Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu +1
Large Language Models (LLM) are a new class of computation engines, "programmed" via prompt engineering. We are still learning how to best "program" these LLMs to help developers.…
Large Language Models and Simple, Stupid Bugs
Kevin Jesse, Toufique Ahmed, Premkumar T. Devanbu +1
With the advent of powerful neural language models, AI-based systems to assist developers in coding tasks are becoming widely available; Copilot is one such system. Copilot uses Co…
Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries
Ali Al-Kaswan, Toufique Ahmed, Maliheh Izadi +3
Reverse engineering binaries is required to understand and analyse programs for which the source code is unavailable. Decompilers can transform the largely unreadable binaries into…