conftrace_
← Application Areas
Machine Learning › Application Areas ›

Efficient Computing

6,876 papers

Papers per year

Papers

CodeFort: Robust Training for Code Generation Models EMNLP 2024
Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts EMNLP 2024
Exploiting Careful Design of SVM Solution for Aspect-term Sentiment Analysis EMNLP 2024
SCA: Selective Compression Attention for Efficiently Extending the Context Window of Large Language Models EMNLP 2024
CapEEN: Image Captioning with Early Exits and Knowledge Distillation EMNLP 2024
An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference EMNLP 2024
LongHeads: Multi-Head Attention is Secretly a Long Context Processor EMNLP 2024
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation EMNLP 2024
LoRAExit: Empowering Dynamic Modulation of LLMs in Resource-limited Settings using Low-rank Adapters EMNLP 2024
MobileQuant: Mobile-friendly Quantization for On-device Language Models EMNLP 2024
VDebugger: Harnessing Execution Feedback for Debugging Visual Programs EMNLP 2024
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning EMNLP 2024
Improving Factual Consistency of News Summarization by Contrastive Preference Optimization EMNLP 2024
Revisiting the Impact of Pursuing Modularity for Code Generation EMNLP 2024
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks EMNLP 2024
Fast Matrix Multiplications for Lookup Table-Quantized LLMs EMNLP 2024
LoRASC: Expressive and Generalizable Low-rank Adaptation for Large Models via Slow Cascaded Learning EMNLP 2024
Temperature-Centric Investigation of Speculative Decoding with Knowledge Distillation EMNLP 2024
Exploring Quantization for Efficient Pre-Training of Transformer Language Models EMNLP 2024
QEFT: Quantization for Efficient Fine-Tuning of LLMs EMNLP 2024
Efficient Active Learning with Adapters EMNLP 2024
Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles EMNLP 2024
From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression EMNLP 2024
VE-KD: Vocabulary-Expansion Knowledge-Distillation for Training Smaller Domain-Specific Language Models EMNLP 2024
Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning Tasks EMNLP 2024