ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2404.03921
  4. Cited By
Simple Techniques for Enhancing Sentence Embeddings in Generative
  Language Models
v1v2 (latest)

Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models

5 April 2024
Bowen Zhang
Kehua Chang
Chunping Li
ArXiv (abs)PDFHTMLGithub (26★)

Papers citing "Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models"

20 / 20 papers shown
Hierarchical Token Prepending: Enhancing Information Flow in Decoder-based LLM Embeddings
Hierarchical Token Prepending: Enhancing Information Flow in Decoder-based LLM Embeddings
Xueying Ding
Xingyue Huang
Mingxuan Ju
Liam Collins
Yozen Liu
Leman Akoglu
Neil Shah
Tong Zhao
175
2
0
18 Nov 2025
AttnCache: Accelerating Self-Attention Inference for LLM Prefill via Attention Cache
AttnCache: Accelerating Self-Attention Inference for LLM Prefill via Attention CacheIACR Cryptology ePrint Archive (IACR ePrint), 2025
Dinghong Song
Yuan Feng
Y. Wang
S. Chen
Cyril Guyot
F. Blagojevic
Hyeran Jeon
Pengfei Su
Dong Li
312
1
0
29 Oct 2025
GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning
GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning
Oussama Gabouj
Kamel Charaf
Ivan Zakazov
Nicolas Mario Baldwin
Robert West
LLMAGRALMLRM
144
0
0
01 Oct 2025
FreeRet: MLLMs as Training-Free Retrievers
FreeRet: MLLMs as Training-Free Retrievers
Yuhan Zhu
Xiangyu Zeng
Chenting Wang
Xinhao Li
Yicheng Xu
Ziang Yan
Yi Wang
Limin Wang
OffRLVLMLRM
238
4
0
29 Sep 2025
What Matters in LLM-Based Feature Extractor for Recommender? A Systematic Analysis of Prompts, Models, and Adaptation
What Matters in LLM-Based Feature Extractor for Recommender? A Systematic Analysis of Prompts, Models, and Adaptation
Kainan Shi
Peilin Zhou
Ge Wang
Han Ding
Fei Wang
183
0
0
18 Sep 2025
Testing the assumptions about the geometry of sentence embedding spaces: the cosine measure need not apply
Testing the assumptions about the geometry of sentence embedding spaces: the cosine measure need not apply
Vivi Nastase
Paola Merlo
117
0
0
01 Sep 2025
Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval
Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval
Yuxiang Liu
Tian Wang
Gourab Kundu
Tianyu Cao
Guang Cheng
Zhen Ge
Jianshu Chen
Qingjun Cui
Trishul Chilimbi
LRM
174
2
0
29 Aug 2025
Context-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment
Context-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment
Dahun Kim
A. Angelova
VLM
332
3
0
03 Aug 2025
Resource-Efficient Adaptation of Large Language Models for Text Embeddings via Prompt Engineering and Contrastive Fine-tuning
Resource-Efficient Adaptation of Large Language Models for Text Embeddings via Prompt Engineering and Contrastive Fine-tuning
Benedikt Roth
Stephan Rappensperger
Tianming Qiu
Hamza Imamović
Julian Wormann
Hao Shen
204
1
0
30 Jul 2025
On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey
On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey
Meishan Zhang
Xin Zhang
X. Zhao
Shouzheng Huang
Baotian Hu
Min Zhang
364
4
0
28 Jul 2025
HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devices
HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple DevicesAnnual Meeting of the Association for Computational Linguistics (ACL), 2025
Silin Li
Yuhang Guo
Jiashu Yao
Zeming Liu
Haifeng Wang
312
8
0
26 May 2025
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time SteeringAnnual Meeting of the Association for Computational Linguistics (ACL), 2025
Zifeng Cheng
Zhonghui Wang
Yuchen Fu
Zhiwei Jiang
Yafeng Yin
Cong Wang
Qing Gu
237
8
0
19 May 2025
CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass
CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward PassAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2025
Bowen Zhang
Zixin Song
Chunping Li
317
3
0
01 May 2025
LLMs are Also Effective Embedding Models: An In-depth Overview
LLMs are Also Effective Embedding Models: An In-depth Overview
Chongyang Tao
Tao Shen
Shen Gao
Junshuo Zhang
Zhen Li
Kai Hua
Wenpeng Hu
Zhengwei Tao
Shuai Ma
550
33
0
17 Dec 2024
Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs
Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMsAnnual Meeting of the Association for Computational Linguistics (ACL), 2024
Yuchen Fu
Zifeng Cheng
Zhiwei Jiang
Zhonghui Wang
Yafeng Yin
Zhengliang Li
Qing Gu
LLMAG
334
12
0
16 Dec 2024
GASE: Generatively Augmented Sentence Encoding
GASE: Generatively Augmented Sentence Encoding
Manuel Frank
Haithem Afli
153
1
0
07 Nov 2024
GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings
GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence EmbeddingsNorth American Chapter of the Association for Computational Linguistics (NAACL), 2024
Raghuveer Thirukovalluru
Bhuwan Dhingra
383
11
0
18 Oct 2024
Instruction Embedding: Latent Representations of Instructions Towards
  Task Identification
Instruction Embedding: Latent Representations of Instructions Towards Task IdentificationNeural Information Processing Systems (NeurIPS), 2024
Yiwei Li
Jiayi Shi
Shaoxiong Feng
Peiwen Yuan
Xinglin Wang
Boyuan Pan
Heda Wang
Yao Hu
Kan Li
245
4
0
29 Sep 2024
Logistic Regression makes small LLMs strong and explainable
  "tens-of-shot" classifiers
Logistic Regression makes small LLMs strong and explainable "tens-of-shot" classifiers
Marcus Buckmann
Edward Hill
320
4
0
06 Aug 2024
Pcc-tuning: Breaking the Contrastive Learning Ceiling in Semantic
  Textual Similarity
Pcc-tuning: Breaking the Contrastive Learning Ceiling in Semantic Textual SimilarityConference on Empirical Methods in Natural Language Processing (EMNLP), 2024
Bowen Zhang
Chunping Li
240
6
0
14 Jun 2024
1
Page 1 of 1