11 accepted papers
Automatic code question answering aims to generate precise answers to questions about code by analyzing code snippets. To provide an appropriate answer, it is necessary to accurately understand the relevant part of the code and correctly interpret the intent of the question. However, in real-world s…
Text-to-SQL, which translates a natural language question into an SQL query, has advanced with in-context learning of Large Language Models (LLMs). However, existing methods show little improvement in performance compared to randomly chosen demonstrations, and significant performance drops when smal…
While Large Language Models (LLMs) excel in zero-shot Question Answering (QA), they tend to expose biases in their internal knowledge when faced with socially sensitive questions, leading to a degradation in performance. Existing zero-shot methods are efficient but failto consider context and preven…
Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even black-box attack methods also require the target model’s output information. They are not applicable in real-world scen…
In various natural language processing (NLP) tasks, fine-tuning Pre-trained Language Models (PLMs) often leads to the issue of spurious correlations, which negatively impacts performance, particularly when dealing with out-of-distribution data.To address this problem, we propose **SALAD** (**S**truc…
Recently, code Pre-trained Language Models (PLMs) trained on large amounts of code and comment, have shown great success in code defect detection tasks. However, most PLMs simply treated the code as a single sequence and only used the encoder of PLMs to determine if there exist defects in the entire…
Code summarization, which aims to automatically generate natural language descriptions from source code, has become an essential task in software development for better program understanding. Abstract Syntax Tree (AST), which represents the syntax structure of the source code, is helpful when utiliz…
In order to solve the inefficient parameter update and storage issues of fine-tuning in Natural Language Generation (NLG) tasks, prompt-tuning methods have emerged as lightweight alternatives. Furthermore, efforts to reduce the gap between pre-training and fine-tuning have shown successful results i…
Automatic processing of source code, such as code clone detection and software vulnerability detection, is very helpful to software engineers. Large pre-trained Programming Language (PL) models (such as CodeBERT, GraphCodeBERT, CodeT5, etc.), show very powerful performance on these tasks. However, t…
As pre-trained models have shown successful performance in program language processing as well as natural language processing, adversarial attacks on these models also attract attention.However, previous works on black-box adversarial attacks generated adversarial examples in a very inefficient way…
In this letter, we present an implementation of deblurring hardware to mitigate blur incurred by optical aberrations in a real-time manner to increase resolution for mobile camera modules. As optical aberrations tend to be variant according to spatial location, algorithm should support spatially var…