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20232026
most citedThe Daunting Dilemma with Sentence Encoders: Success on Standard Benchmarks, Failure in Capturing Basic Semantic Properties

1 citations · 1 across the 2 of their papers we have counts for

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cs.CL2025

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…

cs.CL2025

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL20231 cited

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…