Adila Alfa Krisnadhi

Lucky Susanto, Ryandito Diandaru, Adila Krisnadhi et al.

Neural machine translation (NMT) for low-resource local languages in Indonesia faces significant challenges, including the need for a representative benchmark and limited data availability. This work addresses these challenges by comprehensively analyzing training NMT systems for four low-resource local languages in Indonesia: Javanese, Sundanese, Minangkabau, and Balinese. Our study encompasses various training approaches, paradigms, data sizes, and a preliminary study into using large language models for synthetic low-resource languages parallel data generation. We reveal specific trends and insights into practical strategies for low-resource language translation. Our research demonstrates that despite limited computational resources and textual data, several of our NMT systems achieve competitive performances, rivaling the translation quality of zero-shot gpt-3.5-turbo. These findings significantly advance NMT for low-resource languages, offering valuable guidance for researchers in similar contexts.

Eugenius Mario Situmorang, Adila Alfa Krisnadhi, Ari Wibisono

TextGrad is a novel approach to text-based automatic differentiation that enables composite AI systems to perform optimization without explicit numerical equations. However, it currently lacks self-verification mechanisms that ensure reasoning validity in text-based decision making. This research introduces TextualVerifier, a verification framework that leverages chain-of-thought reasoning and majority voting with large language models to address this verification gap. TextualVerifier implements a four-stage workflow: chain-of-thought decomposition, variant generation, majority voting, and consensus aggregation. It integrates non-invasively with TextGrad at both the loss function and optimization result verification stages. Experimental evaluation using the Gemini 1.5 Pro model is conducted in two phases: (1) standalone evaluation on PRM800K, and (2) integrated evaluation with TextGrad on GPQA-Diamond, MMLU-ML, and MMLU-CP benchmarks. Results show statistically significant improvements (p < 0.001). In phase one, TextualVerifier improves the validity of reasoning steps by 29 percent. In phase two, integration into TextGrad loss function yields a 2.2 percentage point gain from 68.2 to 70.4 percent with a moderate overhead of 5.9 LLM calls on average. Further evaluations of TextualVerifier versioning yield 8.08, 10.71, and 3.92 percentage point improvements on GPQA, MMLU-ML, and MMLU-CP respectively. TextualVerifier thus presents the first self-verification framework for TextGrad through LLM-based techniques without requiring numerical gradients, enabling more reliable reasoning and opening new directions for verification in text-based optimization.

Md. Kamruzzaman Sarker, Adila Krisnadhi, David Carral et al.

It has been argued that it is much easier to convey logical statements using rules rather than OWL (or description logic (DL)) axioms. Based on recent theoretical developments on transformations between rules and DLs, we have developed ROWLTab, a Protege plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL 2 DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule. In this paper, we present ROWLTab, together with a user evaluation of its effectiveness compared to entering axioms using the standard Protege interface. Our evaluation shows that modeling with ROWLTab is much quicker than the standard interface, while at the same time, also less prone to errors for hard modeling tasks.

Md. Kamruzzaman Sarker, Adila A. Krisnadhi, Pascal Hitzler

Once the conceptual overview, in terms of a somewhat informal class diagram, has been designed in the course of engineering an ontology, the process of adding many of the appropriate logical axioms is mostly a routine task. We provide a Protege plugin which supports this task, together with a visual user interface, based on established methods for ontology design pattern modeling.

Md. Kamruzzaman Sarker, David Carral, Adila A. Krisnadhi et al.

In our experience, some ontology users find it much easier to convey logical statements using rules rather than OWL (or description logic) axioms. Based on recent theoretical developments on transformations between rules and description logics, we develop ROWL, a Protege plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule.

Pascal Hitzler, Adila Krisnadhi

We provide a detailed example for modular ontology modeling based on ontology design patterns.