Tootfinder

This https://arxiv.org/abs/2307.09950 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Prompting for Automatic Log Template Extraction
Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing supervised log parsers require model t…

Constrained Decoding for Code Language Models via Efficient Left and Right Quotienting of Context-Sensitive Grammars
Daniel Melcer, Nathan Fulton, Sanjay Krishna Gouda, Haifeng Qian
https://arxiv.org/abs/2402.17988

Constrained Decoding for Code Language Models via Efficient Left and Right Quotienting of Context-Sensitive Grammars
Large Language Models are powerful tools for program synthesis and advanced auto-completion, but come with no guarantee that their output code is syntactically correct. This paper contributes an incremental parser that allows early rejection of syntactically incorrect code, as well as efficient detection of complete programs for fill-in-the-middle (FItM) tasks. We develop Earley-style parsers that operate over left and right quotients of arbitrary context-free grammars, and we extend our increm…

This https://arxiv.org/abs/2402.16311 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Cross-domain Chinese Sentence Pattern Parsing
Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching.Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability.To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework. Partial syntactic rules from a source domain are combined with target domain sentences to dynamically generate training data, enhancing the a…

UVL Sentinel: a tool for parsing and syntactic correction of UVL datasets
David Romero-Organvidez, Jose A. Galindo, David Benavides
https://arxiv.org/abs/2403.18482

UVL Sentinel: a tool for parsing and syntactic correction of UVL datasets
Feature models have become a de facto standard for representing variability in software product lines. UVL (Universal Variability Language) is a language which expresses the features, dependencies, and constraints between them. This language is written in plain text and follows a syntactic structure that needs to be processed by a parser. This parser is software with specific syntactic rules that the language must comply with to be processed correctly. Researchers have datasets with numerous fe…

MoReVQA: Exploring Modular Reasoning Models for Video Question Answering
Juhong Min, Shyamal Buch, Arsha Nagrani, Minsu Cho, Cordelia Schmid
https://arxiv.org/abs/2404.06511

MoReVQA: Exploring Modular Reasoning Models for Video Question Answering
This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage, modular reasoning framework. Previous modular methods have shown promise with a single planning stage ungrounded in visual content. However, through a simple and effective baseline, we find that such systems can lead to brittle behavior in practice for challenging videoQA settings. Thus, unlike traditional single-stage planning methods, we propose a multi-stage system consisting of an event parser,…