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Krish Sharma

Krish Sharma has 5 papers in our library, in 2026, most of them on Reasoning, Word sense disambiguation, Language models & pretraining; often with Jatin Bedi, Lakksh Sharma, Rhea Singhal.

5 papers in our library · 2026

OpenAlex · h-index 1 · 9 works · 1 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 5 times in our library · ⭐ Paper Stars ranking: #10472

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

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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. blue at SMM4H-HeaRD 2026: Class-Weighted Transformer Ensembles with Structured Decoding and Chain-of-Thought Blending across Six Health NLP Shared Tasks (2026 · 2 citations)
    Team blue entered six SMM4H-HeaRD 2026 shared tasks on health text from social media, PubMed, pathology reports and clinical documents.
  2. blue at SemEval-2026 Task 4: Synergizing Long-Context Reranking with Semantic Similarity for Narrative Alignment (2026 · 1 citation)
    Team blue tackled Track A (Pairwise Similarity) of SemEval-2026 Task 4. Given an anchor story and two candidates, a system must pick the candidate closer in abstract theme, course of action and outcome. The authors argue that 512-token encoders such as BERT and RoBERTa often cut off the endings that carry resolutions…
  3. Lakksh at SemEval-2026 Task 11(1 & 2): Neuro-Symbolic Decomposition to Mitigate Content Bias in Syllogistic Reasoning (2026 · 1 citation)
    Large language models tend to judge syllogisms by whether the conclusion sounds believable rather than whether it follows logically.
  4. TELL-TALE: Task Efficient LLMs with Task Aware Layer Elimination (2026 · 1 citation)
    Modern LLMs run every input through all of their layers, yet many layers contribute little or even hurt accuracy on a specific downstream task.
  5. blue at SemEval-2026 Task 5: NarrBERT : Narrative-Aware BERT for Word Sense Disambiguation (2026)
    Team blue's entry to SemEval-2026 Task 5 (AmbiStory) tackles graded word sense disambiguation. Instead of selecting one correct sense, a system must give each candidate sense of an ambiguous word a 1–5 plausibility rating that tracks averaged human judgments. The input is a five-sentence story whose ending may…

 Co-authors here

5 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 5 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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