🧑‍🔬 Archiki Prasad

🏛️ Affiliations

🎓University of North Carolina at Chapel HillChapel Hill · United States2021–2026 · 26 paperscurrent🎓Indian Institute of Technology BombayMumbai · India2020–2021 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models (2023 · 182 citations)
   Instructions written for human annotators often work poorly as prompts for large language models. Manual rewriting is slow and subjective, and gradient-based prompt tuning is expensive and impossible for models available only through an API. The authors propose GrIPS (Gradient-free Instructional Prompt Search). It…
2. ADaPT: As-Needed Decomposition and Planning with Language Models (2024 · 85 citations)
   LLM agents for interactive tasks usually follow one of two patterns. Some choose one action at a time, like ReAct. Others draft a fixed list of sub-tasks up front and then run them (plan-and-execute). Both break down on complex tasks: a single sub-task that proves too hard can sink the whole attempt. ADaPT tackles…
3. ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness (2023 · 68 citations)
   Language models increasingly produce step-by-step rationales, but they are usually judged only by whether the final answer is right.

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language ModelsArchiki Prasad, Peter Hase, Xiang Zhou, et al.Conference of the European Chapter of the… · 182 citations
2. ADaPT: As-Needed Decomposition and Planning with Language ModelsArchiki Prasad, Alexander Koller, Mareike Hartmann, et al.Findings of the Association for Computational… · 85 citations
3. ReCEval: Evaluating Reasoning Chains via Correctness and InformativenessArchiki Prasad, Swarnadeep Saha, Xiang Zhou, et al.Conference on Empirical Methods in Natural… · 68 citations
4. MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning (original ↗)Justin Chen et al.2025 · Proceedings of the 2025 Conference on Empirical… · 20 citations · no summary yet
5. Multi-Attribute Steering of Language Models via Targeted Intervention (original ↗)Duy Nguyen et al.2025 · Proceedings of the 63rd Annual Meeting of the… · 11 citations · no summary yet
6. GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs (original ↗)Duy Nguyen et al.2026 · Proceedings of the 64th Annual Meeting of the… · 4 citations · no summary yet

🤝 Co-authors here

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

• Mohit Bansal ×3
• Xiang Zhou ×2
• Alexander Koller
• Ashish Sabharwal
• Mareike Hartmann
• Peter Clark
• Peter Hase
• Swarnadeep Saha
• Tushar Khot

Venues

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

• Conference on Empirical Methods in Natural Language… ×2: 2023
• Proceedings of the 63rd Annual Meeting of the Association… ×2
• Conference of the European Chapter of the Association for…: 2023
• Findings of the Association for Computational Linguistics: 2024

🔗 Cited by

Papers of our library cite their works 243 times (of the 141,840 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated 24 October 2026

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