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Srikanth Doss

Srikanth Doss has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Nikolaos Pappas, Zheng Qi, Evangelia Spiliopoulou.

2 papers in our library · 2026

OpenAlex · h-index 2 · 15 works · 92 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 38 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #13136

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

🏢AmazonSeattle · United States2022–2026 · 6 paperscurrent🎓University of California San DiegoSan Diego · United States2026 · 1 papercurrent

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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MEAV: Model Editing with Alignment Vectors for inference time LLM alignment in single and multidomain preference spectrum (2026 · 1 citation)
    The paper addresses the cost of adjusting LLM alignment after training. Methods like RLHF or DPO need retraining for every new preference setting, and many inference-time alternatives rely on a reward model at each step. The authors propose MEAV, which treats alignment as model editing in the spirit of Task…
  2. Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning (2026)
    LLMs used for decisions such as content moderation or clinical support need confidence scores that tell downstream systems when to fall back to a larger model or a human.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Label Semantics for Few Shot Named Entity Recognition ↗Findings of the Association for Computational Linguistics: ACL 2022 · no summary yetJie Ma et al.2022 · 123 citations
  2. MEAV: Model Editing with Alignment Vectors for inference time LLM alignment in single and multidomain preference spectrumFindings of the Association for Computational LinguisticsSadat Shahriar, Zheng Qi, Nikolaos Pappas, et al.2026 · 1 citation
  3. Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement LearningFindings of the Association for Computational LinguisticsDuygu Nur Yaldiz, Evangelia Spiliopoulou, Zheng Qi, et al.2026

 Co-authors here

7 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 38 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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