4 citations · 7 across the 6 of their papers we have counts for
6 papers
TopoTuner: Topological Finetuning of Large Language Models
Abdulkadir Erol, Yash Mahajan, Vepaul Hariprashad +4
Full fine-tuning remains a strong way to adapt pretrained LLMs, but it updates all weights and can be expensive. LoRA reduces the number of trainable parameters, but it does not di…
Revisiting Word Embeddings in the LLM Era
Yash Mahajan, Matthew Freestone, Sathyanarayanan Aakur +1
Large Language Models (LLMs) have recently shown remarkable advancement in various NLP tasks. As such, a popular trend has emerged lately where NLP researchers extract word/sentenc…
Set-Theoretic Compositionality of Sentence Embeddings
Naman Bansal, Yash mahajan, Sanjeev Sinha +1
Sentence encoders play a pivotal role in various NLP tasks; hence, an accurate evaluation of their compositional properties is paramount. However, existing evaluation methods predo…
LLMs as Meta-Reviewers' Assistants: A Case Study
Eftekhar Hossain, Sanjeev Kumar Sinha, Naman Bansal +11
One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves assimilating diverse opinions from multiple expert peer…
Revisiting Word Embeddings in the LLM Era
Yash Mahajan, Matthew Freestone, Naman Bansal +2
Large Language Models (LLMs) have recently shown remarkable advancement in various NLP tasks. As such, a popular trend has emerged lately where NLP researchers extract word/sentenc…
The Daunting Dilemma with Sentence Encoders: Success on Standard Benchmarks, Failure in Capturing Basic Semantic Properties
Yash Mahajan, Naman Bansal, Shubhra Kanti Karmaker
In this paper, we adopted a retrospective approach to examine and compare five existing popular sentence encoders, i.e., Sentence-BERT, Universal Sentence Encoder (USE), LASER, Inf…