Saved in:
| Main Author: | Pandey, Sanchit |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.11513 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Quecto-V1: Empirical Analysis of 8-bit Quantized Small Language Models for On-Device Legal Retrieval
by: Dikshit, Subrit
Published: (2026)
by: Dikshit, Subrit
Published: (2026)
Retrieving Counterfactuals Improves Visual In-Context Learning
by: Xiong, Guangzhi, et al.
Published: (2026)
by: Xiong, Guangzhi, et al.
Published: (2026)
Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models
by: Ahuja, Sanchit, et al.
Published: (2026)
by: Ahuja, Sanchit, et al.
Published: (2026)
Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
by: Tan, Jiejun, et al.
Published: (2024)
by: Tan, Jiejun, et al.
Published: (2024)
Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models
by: Buhnila, Ioana, et al.
Published: (2024)
by: Buhnila, Ioana, et al.
Published: (2024)
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
by: Ahuja, Sanchit, et al.
Published: (2023)
by: Ahuja, Sanchit, et al.
Published: (2023)
Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders
by: Xiong, Guangzhi, et al.
Published: (2025)
by: Xiong, Guangzhi, et al.
Published: (2025)
Dense X Retrieval: What Retrieval Granularity Should We Use?
by: Chen, Tong, et al.
Published: (2023)
by: Chen, Tong, et al.
Published: (2023)
An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought
by: Zhao, Yuetong, et al.
Published: (2024)
by: Zhao, Yuetong, et al.
Published: (2024)
MarsRetrieval: Benchmarking Vision-Language Models for Planetary-Scale Geospatial Retrieval on Mars
by: Wang, Shuoyuan, et al.
Published: (2026)
by: Wang, Shuoyuan, et al.
Published: (2026)
Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment
by: Luo, Kun, et al.
Published: (2024)
by: Luo, Kun, et al.
Published: (2024)
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation
by: Yuan, Xujie, et al.
Published: (2025)
by: Yuan, Xujie, et al.
Published: (2025)
Scaling Laws for Multilingual Language Models
by: He, Yifei, et al.
Published: (2024)
by: He, Yifei, et al.
Published: (2024)
When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
by: Labruna, Tiziano, et al.
Published: (2024)
by: Labruna, Tiziano, et al.
Published: (2024)
Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models
by: Weller, Orion, et al.
Published: (2024)
by: Weller, Orion, et al.
Published: (2024)
To Case or Not to Case: An Empirical Study in Learned Sparse Retrieval
by: Lionis, Emmanouil Georgios, et al.
Published: (2026)
by: Lionis, Emmanouil Georgios, et al.
Published: (2026)
An Empirical Study of SFT-DPO Interaction and Parameterization in Small Language Models
by: Feng, Yuming, et al.
Published: (2026)
by: Feng, Yuming, et al.
Published: (2026)
Characterizing Model Behavior Under Synthetic Data Training: An Empirical Study Across Scales and Mixing Ratios
by: Du, Y., et al.
Published: (2025)
by: Du, Y., et al.
Published: (2025)
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models
by: Cheng, Pengzhou, et al.
Published: (2024)
by: Cheng, Pengzhou, et al.
Published: (2024)
Investigating Spatial Attention Bias in Vision-Language Models
by: Chaudhary, Aryan, et al.
Published: (2025)
by: Chaudhary, Aryan, et al.
Published: (2025)
Fast and Slow Generating: An Empirical Study on Large and Small Language Models Collaborative Decoding
by: Zhang, Kaiyan, et al.
Published: (2024)
by: Zhang, Kaiyan, et al.
Published: (2024)
Enhancing Test-Time Scaling of Large Language Models with Hierarchical Retrieval-Augmented MCTS
by: Dou, Alex ZH, et al.
Published: (2025)
by: Dou, Alex ZH, et al.
Published: (2025)
A MapReduce Approach to Effectively Utilize Long Context Information in Retrieval Augmented Language Models
by: Zhang, Gongbo, et al.
Published: (2024)
by: Zhang, Gongbo, et al.
Published: (2024)
Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval
by: Ji, Luo, et al.
Published: (2024)
by: Ji, Luo, et al.
Published: (2024)
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
by: Lee, Jinhyuk, et al.
Published: (2024)
by: Lee, Jinhyuk, et al.
Published: (2024)
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
by: Li, Mingda, et al.
Published: (2024)
by: Li, Mingda, et al.
Published: (2024)
Can Language Model Understand Word Semantics as A Chatbot? An Empirical Study of Language Model Internal External Mismatch
by: Zhao, Jinman, et al.
Published: (2024)
by: Zhao, Jinman, et al.
Published: (2024)
Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models
by: Park, Seong-Il, et al.
Published: (2024)
by: Park, Seong-Il, et al.
Published: (2024)
Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models
by: Maekawa, Seiji, et al.
Published: (2024)
by: Maekawa, Seiji, et al.
Published: (2024)
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
by: Zhang, Taolin, et al.
Published: (2024)
by: Zhang, Taolin, et al.
Published: (2024)
What Drives Cross-lingual Ranking? Retrieval Approaches with Multilingual Language Models
by: Goworek, Roksana, et al.
Published: (2025)
by: Goworek, Roksana, et al.
Published: (2025)
On Retrieval Augmentation and the Limitations of Language Model Training
by: Chiang, Ting-Rui, et al.
Published: (2023)
by: Chiang, Ting-Rui, et al.
Published: (2023)
MINERS: Multilingual Language Models as Semantic Retrievers
by: Winata, Genta Indra, et al.
Published: (2024)
by: Winata, Genta Indra, et al.
Published: (2024)
The Compressor-Retriever Architecture for Language Model OS
by: Yang, Yuan, et al.
Published: (2024)
by: Yang, Yuan, et al.
Published: (2024)
Groundedness in Retrieval-augmented Long-form Generation: An Empirical Study
by: Stolfo, Alessandro
Published: (2024)
by: Stolfo, Alessandro
Published: (2024)
Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets
by: Pavlidis, Nikolaos, et al.
Published: (2025)
by: Pavlidis, Nikolaos, et al.
Published: (2025)
Distilling LLM Agent into Small Models with Retrieval and Code Tools
by: Kang, Minki, et al.
Published: (2025)
by: Kang, Minki, et al.
Published: (2025)
Big Reasoning with Small Models: Instruction Retrieval at Inference Time
by: Alkiek, Kenan, et al.
Published: (2025)
by: Alkiek, Kenan, et al.
Published: (2025)
Can Large Language Models Understand DL-Lite Ontologies? An Empirical Study
by: Wang, Keyu, et al.
Published: (2024)
by: Wang, Keyu, et al.
Published: (2024)
Byte-Exact Deduplication in Retrieval-Augmented Generation: A Three-Regime Empirical Analysis Across Public Benchmarks
by: Schelpe, Sietse
Published: (2026)
by: Schelpe, Sietse
Published: (2026)
Similar Items
-
Quecto-V1: Empirical Analysis of 8-bit Quantized Small Language Models for On-Device Legal Retrieval
by: Dikshit, Subrit
Published: (2026) -
Retrieving Counterfactuals Improves Visual In-Context Learning
by: Xiong, Guangzhi, et al.
Published: (2026) -
Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models
by: Ahuja, Sanchit, et al.
Published: (2026) -
Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
by: Tan, Jiejun, et al.
Published: (2024) -
Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models
by: Buhnila, Ioana, et al.
Published: (2024)