26 citations · 30 across the 6 of their papers we have counts for
6 papers
Cross-Architecture Steering Transfer in Language Models: A Systematic Empirical Study
Ayushi Agarwal
Independently trained large language models may develop shared internal representations of semantic concepts despite architectural differences -- but whether this geometric similar…
PoolBench: A Benchmark for Pooling Strategies in Concept Representation Evaluation for Decoder-Only LLMs
Ayushi Agarwal
Pooling is a consequential but under-examined design choice in decoder-only concept representation work: practitioners must collapse token-level hidden states into a passage-level…
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI
Sourav Banerjee, Anush Mahajan, Ayushi Agarwal +1
Natural Language Inference (NLI) tasks require identifying the relationship between sentence pairs, typically classified as entailment, contradiction, or neutrality. While the curr…
The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance?
Sourav Banerjee, Ayushi Agarwal, Eishkaran Singh
The pursuit of leaderboard rankings in Large Language Models (LLMs) has created a fundamental paradox: models excel at standardized tests while failing to demonstrate genuine langu…
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR
Sourav Banerjee, Ayushi Agarwal, Promila Ghosh
Automatic Speech Recognition (ASR) systems in the clinical domain face significant challenges, notably the need to recognise specialised medical vocabulary accurately and meet stri…
LLMs Will Always Hallucinate, and We Need to Live With This
Sourav Banerjee, Ayushi Agarwal, Saloni Singla
As Large Language Models become more ubiquitous across domains, it becomes important to examine their inherent limitations critically. This work argues that hallucinations in langu…