Papers by Sshubam Verma

2 papers
Finding Blind Spots in Evaluator LLMs with Interpretable Checklists (2024.emnlp-main)

Copied to clipboard

Challenge: Large Language Models are increasingly relied upon to evaluate text outputs of other LLMs . however, concerns persist over the accuracy of these assessments and the potential for misleading conclusions.
Approach: They propose a framework to assess the reliability of Large Language Models (LLMs) they propose ' FBI' framework to examine the proficiency of Evaluator LLMs in assessing four critical abilities .
Outcome: The proposed framework assesses the performance of LLMs in text generation tasks.
MILU: A Multi-task Indic Language Understanding Benchmark (2025.naacl-long)

Copied to clipboard

Challenge: Existing benchmarks focus on English, leaving substantial gaps in assessing LLM capabilities in low-resource and linguistically diverse languages.
Approach: They propose a multi-task indic language understanding benchmark to assess LLMs in low-resource languages.
Outcome: The new benchmark spans 8 domains and 41 subjects across 11 Indic languages, reflecting general and culturally specific knowledge.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations