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Shivank Garg

Shivank Garg has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Pratham Singla, Ayush Singh.

3 papers in our library · 2024–2026

OpenAlex · h-index 1 · 14 works · 2 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 6 times in our library · ⭐ Paper Stars ranking: #10370

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

 Affiliations

🎓Indian Institute of Technology RoorkeeRoorkee · India2023–2026 · 9 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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 · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksApplied Mathematics · 1 workMathematics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksBias, fairness & safety · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Give me a hint: Can LLMs take a hint to solve math problems? (2024 · 6 citations)
    The authors ask whether LLMs solve competition-style math better when they receive a short, problem-specific nudge rather than worked examples.
  2. The Inner Monologue of Language Models: When Reasoning Traces Reveal More Than They Hide (2026)
    Reasoning-focused LLMs generate visible "thinking" text before answering. Oversight methods such as CoT monitoring assume this text honestly reflects the final decision. The authors test that assumption. They also ask whether post-trained models can recognize the hidden behaviors their training gave them.
  3. When Prompt Optimization Becomes Jailbreaking: Adaptive Red-Teaming of Large Language Models (2026)
    Safety benchmarks usually test LLMs against a fixed set of harmful prompts. That approach ignores attackers who refine their inputs after seeing how a model responds. This student-workshop paper asks whether black-box prompt optimizers, built to improve benign task performance, can be repurposed as automated…

 Co-authors here

2 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 6 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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