Demo Data from VLDB 2026 / reviewers in the wild / expert
Aastha A K. Verma
dblp:414/6606
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Representation and self-supervised learning · 77% Language models and text generation · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Representation and self-supervised learning › text embedding › text representation learning
cross-lingual representation learning |
1.0 | 1 | 2026 | Multilingual Language Models Encode Script Over Linguistic Structure · ACL (1) 2026 |
Natural language and speech › Language models and text generation
multilingual language models |
0.3 | 1 | 2026 | Multilingual Language Models Encode Script Over Linguistic Structure · ACL (1) 2026 |
Methods — techniques the papers use, named apart from their topics
sparse autoencoder · 1.0probing · 1.0causal intervention · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Multilingual Language Models Encode Script Over Linguistic StructureabstractMultilingual language models (LMs) organize representations for typologically and orthographically diverse languages into a shared parameter space, yet the nature of this internal organization remains elusive.In this work, we investigate which linguistic properties -abstract language identity or surface-form cues -shape multilingual representations.To do so, we analyze language-associated units across different model families and scales using the Language Activation Probability Entropy (LAPE) metric, and further decompose activations with Sparse Autoencoders.We find that these units are strongly conditioned on orthography: romanization induces near-disjoint representations that align with neither native-script inputs nor English, while word-order shuffling has limited effect on unit identity.Probing shows that typological structure becomes increasingly accessible in deeper layers, while causal interventions indicate that generation is most sensitive to units that are invariant to surface-form perturbations rather than to units identified by typological alignment alone.Overall, our results suggest that multilingual LMs organize representations around surface form, with linguistic abstraction emerging gradually without collapsing into a unified interlingua. Aastha A K. Verma, Anwoy Chatterjee, Tanmoy Chakraborty 0002 |
ACL (1) | 1 |