Posts
All the articles I've posted.
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Communicating Activations Between Language Model Agents
This paper introduces Activation Communication (AC), a novel method for inter-LLM communication using intermediate activations instead of natural language, achieving up to 27% performance improvement over traditional methods with significantly reduced compute across coordination games and reasoning benchmarks.
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Merge to Mix: Mixing Datasets via Model Merging
本文提出*Merge to Mix*方法,通过模型合并技术作为代理,高效选择数据集混合用于大型模型微调,在图像分类和语言任务中显著优于传统方法,接近甚至部分超过Oracle性能。
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MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning
本文提出MELoRA,通过并行堆叠多个小型LoRA模块实现更高的等效秩,以更少的参数在自然语言理解和指令跟随任务上显著优于LoRA。
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Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition
本文通过线性探查和神经元激活分析,复制并扩展了对密集检索模型中预训练与微调知识获取作用的研究,发现预训练知识在DPR模型中主导检索效果且微调导致知识分散,但此结论在不同架构(如Contriever、RepLlama)和表示策略下并不成立。
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Scalable Model Merging with Progressive Layer-wise Distillation
本文提出ProDistill算法,通过逐层教师-学生蒸馏高效合并大型预训练模型,理论证明领域特定数据的必要性,并在视觉、语言任务上实现显著性能提升(6.14%-6.61%),展现出优越的内存和计算效率。