Elamur Research⌕
☰
home / authors / Wicaksono Leksono Muhamad

Wicaksono Leksono Muhamad

Wicaksono Leksono Muhamad has 2 papers in our library, in 2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Reasoning; often with Joanito Agili Lopo, Muhammad Ravi Shulthan Habibi, Samuel Cahyawijaya.

2 papers in our library · 2026

No OpenAlex profile matched yet.

📄 2 works in our library · 🔗 Cited 2 times in our library · ⭐ Paper Stars ranking: #11300

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksDevelopmental and Educational Psychology · 1 workPsychology · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMs (2026 · 1 citation)
    Large language models often decide whether a syllogism is valid based on how plausible its content sounds rather than on its logical form. This content effect is worse in multilingual settings. The ITLC team's SemEval-2026 Task 11 system tackles it by splitting the work between an LLM and a symbolic step:
  2. TOEBM at BEA 2026 Shared Task 1: Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling (2026 · 1 citation)
    The paper tackles L1-aware lexical difficulty prediction for BEA 2026 Shared Task 1. The goal is to estimate how hard an English word is for learners with a particular first language. The authors argue that training with only a scalar regression target leaves the embedding space unstructured. In that setting, items…

 Co-authors here

3 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 2 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/wicaksono-leksono-muhamad/