SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Maurya, Kaushal Kumar, Srivatsa, KV Aditya, Kochmar, Ekaterina |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing
by: Srivatsa, KV Aditya, et al.
Published: (2024)
by: Srivatsa, KV Aditya, et al.
Published: (2024)
LLMs cannot spot math errors, even when allowed to peek into the solution
by: Srivatsa, KV Aditya, et al.
Published: (2025)
by: Srivatsa, KV Aditya, et al.
Published: (2025)
Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?
by: Srivatsa, KV Aditya, et al.
Published: (2025)
by: Srivatsa, KV Aditya, et al.
Published: (2025)
Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors
by: Maurya, Kaushal Kumar, et al.
Published: (2024)
by: Maurya, Kaushal Kumar, et al.
Published: (2024)
Simulating LLM-to-LLM Tutoring for Multilingual Math Feedback
by: Tonga, Junior Cedric, et al.
Published: (2025)
by: Tonga, Junior Cedric, et al.
Published: (2025)
What Makes Math Word Problems Challenging for LLMs?
by: Srivatsa, KV Aditya, et al.
Published: (2024)
by: Srivatsa, KV Aditya, et al.
Published: (2024)
Findings of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors
by: Kochmar, Ekaterina, et al.
Published: (2025)
by: Kochmar, Ekaterina, et al.
Published: (2025)
Pedagogy-driven Evaluation of Generative AI-powered Intelligent Tutoring Systems
by: Maurya, Kaushal Kumar, et al.
Published: (2025)
by: Maurya, Kaushal Kumar, et al.
Published: (2025)
AITutor-EvalKit: Exploring the Capabilities of AI Tutors
by: Naeem, Numaan, et al.
Published: (2025)
by: Naeem, Numaan, et al.
Published: (2025)
SelectLLM: Can LLMs Select Important Instructions to Annotate?
by: Parkar, Ritik Sachin, et al.
Published: (2024)
by: Parkar, Ritik Sachin, et al.
Published: (2024)
LLMs in Education: Novel Perspectives, Challenges, and Opportunities
by: Alhafni, Bashar, et al.
Published: (2024)
by: Alhafni, Bashar, et al.
Published: (2024)
Opportunities and Challenges of LLMs in Education: An NLP Perspective
by: Vajjala, Sowmya, et al.
Published: (2025)
by: Vajjala, Sowmya, et al.
Published: (2025)
A Fully Automated Pipeline for Conversational Discourse Annotation: Tree Scheme Generation and Labeling with Large Language Models
by: Petukhova, Kseniia, et al.
Published: (2025)
by: Petukhova, Kseniia, et al.
Published: (2025)
Towards Reward Modeling for AI Tutors in Math Mistake Remediation
by: Petukhova, Kseniia, et al.
Published: (2026)
by: Petukhova, Kseniia, et al.
Published: (2026)
REFeREE: A REference-FREE Model-Based Metric for Text Simplification
by: Huang, Yichen, et al.
Published: (2024)
by: Huang, Yichen, et al.
Published: (2024)
UNVEILING: What Makes Linguistics Olympiad Puzzles Tricky for LLMs?
by: Choudhary, Mukund, et al.
Published: (2025)
by: Choudhary, Mukund, et al.
Published: (2025)
Intent Matters: Enhancing AI Tutoring with Fine-Grained Pedagogical Intent Annotation
by: Petukhova, Kseniia, et al.
Published: (2025)
by: Petukhova, Kseniia, et al.
Published: (2025)
Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation
by: Barakat, Mariam, et al.
Published: (2026)
by: Barakat, Mariam, et al.
Published: (2026)
How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes
by: Elkins, Sabina, et al.
Published: (2024)
by: Elkins, Sabina, et al.
Published: (2024)
PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?
by: Petukhova, Kseniia, et al.
Published: (2024)
by: Petukhova, Kseniia, et al.
Published: (2024)
PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations
by: Kazakov, Roman, et al.
Published: (2024)
by: Kazakov, Roman, et al.
Published: (2024)
What Makes Cryptic Crosswords Challenging for LLMs?
by: Sadallah, Abdelrahman, et al.
Published: (2024)
by: Sadallah, Abdelrahman, et al.
Published: (2024)
LLM-Select: Feature Selection with Large Language Models
by: Jeong, Daniel P., et al.
Published: (2024)
by: Jeong, Daniel P., et al.
Published: (2024)
PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model Inference
by: Jin, Weisheng, et al.
Published: (2025)
by: Jin, Weisheng, et al.
Published: (2025)
CharSpan: Utilizing Lexical Similarity to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages
by: Maurya, Kaushal Kumar, et al.
Published: (2023)
by: Maurya, Kaushal Kumar, et al.
Published: (2023)
Are LLMs Good Cryptic Crossword Solvers?
by: Sadallah, Abdelrahman, et al.
Published: (2024)
by: Sadallah, Abdelrahman, et al.
Published: (2024)
HFS: Holistic Query-Aware Frame Selection for Efficient Video Reasoning
by: Yang, Yiqing, et al.
Published: (2025)
by: Yang, Yiqing, et al.
Published: (2025)
Importance-Aware Data Selection for Efficient LLM Instruction Tuning
by: Jiang, Tingyu, et al.
Published: (2025)
by: Jiang, Tingyu, et al.
Published: (2025)
Neuron-Aware Data Selection In Instruction Tuning For Large Language Models
by: Chen, Xin, et al.
Published: (2026)
by: Chen, Xin, et al.
Published: (2026)
Listen, Correct, and Feed Back: Spoken Pedagogical Feedback Generation
by: Liang, Junhong, et al.
Published: (2026)
by: Liang, Junhong, et al.
Published: (2026)
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
by: Wu, Xingyu, et al.
Published: (2023)
by: Wu, Xingyu, et al.
Published: (2023)
SectEval: Evaluating the Latent Sectarian Preferences of Large Language Models
by: Maheshwari, Aditya, et al.
Published: (2026)
by: Maheshwari, Aditya, et al.
Published: (2026)
InferCept: Efficient Intercept Support for Augmented Large Language Model Inference
by: Abhyankar, Reyna, et al.
Published: (2024)
by: Abhyankar, Reyna, et al.
Published: (2024)
SCAR: Data Selection via Style Consistency-Aware Response Ranking for Efficient Instruction-Tuning of Large Language Models
by: Li, Zhuang, et al.
Published: (2024)
by: Li, Zhuang, et al.
Published: (2024)
ParamBench: A Graduate-Level Benchmark for Evaluating LLM Understanding on Indic Subjects
by: Maheshwari, Ayush, et al.
Published: (2025)
by: Maheshwari, Ayush, et al.
Published: (2025)
KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answering
by: Zheng, Xinxin, et al.
Published: (2024)
by: Zheng, Xinxin, et al.
Published: (2024)
Efficient Code LLM Training via Distribution-Consistent and Diversity-Aware Data Selection
by: Lyu, Weijie, et al.
Published: (2025)
by: Lyu, Weijie, et al.
Published: (2025)
Evaluating Large Language Models for Material Selection
by: Grandi, Daniele, et al.
Published: (2024)
by: Grandi, Daniele, et al.
Published: (2024)
Large Language Model-guided Document Selection
by: Kong, Xiang, et al.
Published: (2024)
by: Kong, Xiang, et al.
Published: (2024)
IndicParam: Benchmark to evaluate LLMs on low-resource Indic Languages
by: Maheshwari, Ayush, et al.
Published: (2025)
by: Maheshwari, Ayush, et al.
Published: (2025)
Similar Items
-
Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing
by: Srivatsa, KV Aditya, et al.
Published: (2024) -
LLMs cannot spot math errors, even when allowed to peek into the solution
by: Srivatsa, KV Aditya, et al.
Published: (2025) -
Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?
by: Srivatsa, KV Aditya, et al.
Published: (2025) -
Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors
by: Maurya, Kaushal Kumar, et al.
Published: (2024) -
Simulating LLM-to-LLM Tutoring for Multilingual Math Feedback
by: Tonga, Junior Cedric, et al.
Published: (2025)