Cover Image

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lu, Xiaoding, Liu, Zongyi, Liusie, Adian, Raina, Vyas, Mudupalli, Vineet, Zhang, Yuwen, Beauchamp, William
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment
by: Raina, Vyas, et al.
Published: (2024)

Finetuning LLMs for Comparative Assessment Tasks
by: Raina, Vatsal, et al.
Published: (2024)

Efficient LLM Comparative Assessment: a Product of Experts Framework for Pairwise Comparisons
by: Liusie, Adian, et al.
Published: (2024)

LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models
by: Liusie, Adian, et al.
Published: (2023)

Embedding the Teacher: Distilling vLLM Preferences for Scalable Image Retrieval
by: He, Eric, et al.
Published: (2025)

WaterJudge: Quality-Detection Trade-off when Watermarking Large Language Models
by: Molenda, Piotr, et al.
Published: (2024)

Teacher-Student Training for Debiasing: General Permutation Debiasing for Large Language Models
by: Liusie, Adian, et al.
Published: (2024)

Agents Are All You Need for LLM Unlearning
by: Sanyal, Debdeep, et al.
Published: (2025)

Sparsity May Be All You Need: Sparse Random Parameter Adaptation
by: Rios, Jesus, et al.
Published: (2025)

Contrast Is All You Need
by: Kilic, Burak, et al.
Published: (2023)

Training on the Benchmark Is Not All You Need
by: Ni, Shiwen, et al.
Published: (2024)

Yuan3.0 Ultra: A Trillion-Parameter Enterprise-Oriented MoE LLM
by: ai, YuanLab., et al.
Published: (2026)

Attention Smoothing Is All You Need For Unlearning
by: Zade, Saleh Zare, et al.
Published: (2026)

Rho-1: Not All Tokens Are What You Need
by: Lin, Zhenghao, et al.
Published: (2024)

Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication
by: Ma, Weicheng, et al.
Published: (2025)

English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training
by: Dhaliwal, Mehak, et al.
Published: (2026)

Investigating the Emergent Audio Classification Ability of ASR Foundation Models
by: Ma, Rao, et al.
Published: (2023)

Tensor Product Attention Is All You Need
by: Zhang, Yifan, et al.
Published: (2025)

More Agents Is All You Need
by: Li, Junyou, et al.
Published: (2024)

Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning
by: Lu, Jinghui, et al.
Published: (2025)

Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs
by: Wu, Zongqian, et al.
Published: (2025)

SurfaceLogicKV: Surface and Logic Attention Behaviors are All You Need for Robust KV Cache Compression
by: Li, Mengjie, et al.
Published: (2025)

Evaluation is All You Need: Strategic Overclaiming of LLM Reasoning Capabilities Through Evaluation Design
by: Sun, Lin, et al.
Published: (2025)

SecEncoder: Logs are All You Need in Security
by: Bulut, Muhammed Fatih, et al.
Published: (2024)

Self-Verification is All You Need To Pass The Japanese Bar Examination
by: Shin, Andrew
Published: (2026)

Not All Documents Are What You Need for Extracting Instruction Tuning Data
by: Zhang, Chi, et al.
Published: (2025)

Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs
by: Labroo, Arya, et al.
Published: (2026)

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
by: Bai, Yuelin, et al.
Published: (2024)

Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
by: Bai, Yuqi, et al.
Published: (2025)

All You Need is One: Capsule Prompt Tuning with a Single Vector
by: Liu, Yiyang, et al.
Published: (2025)

Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding
by: Li, Ruanjun, et al.
Published: (2025)

Synthetic Data RL: Task Definition Is All You Need
by: Guo, Yiduo, et al.
Published: (2025)

Attention Is All You Need for KV Cache in Diffusion LLMs
by: Nguyen-Tri, Quan, et al.
Published: (2025)

Rethinking Data Selection at Scale: Random Selection is Almost All You Need
by: Xia, Tingyu, et al.
Published: (2024)

Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems
by: Barker, Matthew, et al.
Published: (2025)

Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
by: Chen, Lingjiao, et al.
Published: (2024)

Trillion 7B Technical Report
by: Han, Sungjun, et al.
Published: (2025)

CrossCheckGPT: Universal Hallucination Ranking for Multimodal Foundation Models
by: Sun, Guangzhi, et al.
Published: (2024)

Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?
by: Santosh, T. Y. S. S, et al.
Published: (2024)

Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning
by: Som, Anirudh, et al.
Published: (2023)