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.
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
Affiliations
Fields, topics and concepts
📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 2 worksBiochemistry, Genetics and Molecular Biology · 1 workMolecular Biology · 1 work
🧭 Topics 4Research subjects and languages the work is about.Reasoning · 2 worksInformation retrieval & search · 1 workLanguage models & pretraining · 1 workWord sense disambiguation · 1 work
Works by year
2020s
Papers
- 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. - 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… - 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. - 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. - 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
Venues
- International Workshop on Semantic Evaluation (SemEval) ×3: SemEval 2026 ×3
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- Social Media Mining for Health Research and Applications Workshop and Shared Tasks: Social Media Mining for Health Research and Applications Workshop and Shared Tasks 2026
Cited by
Updated
This page in Markdown → https://md.research.elamur.ai/free/en/authors/krish-sharma/