Papers by Masoud Hashemi
Auto-Cypher: Improving LLMs on Cypher generation via LLM-supervised generation-verification framework (2025.naacl-short)
Copied to clipboard
| Challenge: | Graph databases like Neo4j are gaining popularity for handling complex, interconnected data, over traditional relational databases. |
| Approach: | They propose an automated pipeline to generate Cypher queries for Neo4j using LLM-As-Database-Filler, a novel strategy for ensuring Cyphere query correctness. |
| Outcome: | The proposed pipeline generates high quality Cypher data containing 29.8k instances across various domains and queries with varying complexities. |
Prompting with Phonemes: Enhancing LLMs’ Multilinguality for Non-Latin Script Languages (2025.naacl-long)
Copied to clipboard
Hoang H Nguyen, Khyati Mahajan, Vikas Yadav, Julian Salazar, Philip S. Yu, Masoud Hashemi, Rishabh Maheshwary
| Challenge: | Multilingual LLMs have achieved remarkable benchmark performance, but continue to underperform on non-Latin script languages. |
| Approach: | They propose to integrate phonemic transcriptions as complementary signals to induce script-invariant representations by integrating phonemic and orthographic transcriptions. |
| Outcome: | The proposed approach improves performance for Latin and non-Latin script languages, with 12.6% performance improvement and 15.1% performance improvement compared to randomized ICL retrieval. |
Controllable Clustering with LLM-driven Embeddings (2025.emnlp-industry)
Copied to clipboard
Kerria Pang-Naylor, Shivani Manivasagan, Aitong Zhong, Mehak Garg, Nicholas Mondello, Blake Buckner, Jonathan P. Chang, Khyati Mahajan, Masoud Hashemi, Fabio Casati
| Challenge: | Unsupervised text clustering is unlikely to produce groupings that work across use cases . authors present techniques to effectively control text embeddings with minimal human input . |
| Approach: | They propose techniques to control text embeddings with minimal human input . they evaluate clustering performance for datasets with multiple independent labels . |
| Outcome: | The proposed techniques improve clustering for one perspective or use case, but at a tradeoff in performance for another use case. |
Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Abstention Ability (AA) is a critical aspect of Large Language Model (LLM) reliability. |
| Approach: | They propose a black-box evaluation approach and a new dataset, Abstain-QA, to rigorously assess AA across varied question types, domains, and task types. |
| Outcome: | The proposed evaluation process and new dataset, Abstain-QA, are crafted to rigorously assess AA across varied question types, domains, and task types. |