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Found 6 papers, 1 papers with code
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
Ranked #5 on
MMR total
on MRR-Benchmark
(using extra training data)
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
In this paper, we introduce Direct Nash Optimization (DNO), a provable and scalable algorithm that marries the simplicity and stability of contrastive learning with theoretical generality from optimizing general preferences.
Adapting LLM Agents with Universal Feedback in Communication
To optimize agent interactions for task-specific learning with our universal buffer and pipeline, we introduce diverse communication patterns tailored for both single-agent and multi-agent environments.
Efficient RLHF: Reducing the Memory Usage of PPO
To address this issue, we present a comprehensive analysis the memory usage, performance, and training time of memory-savings techniques for PPO.
What Matters In The Structured Pruning of Generative Language Models?
Auto-regressive large language models such as GPT-3 require enormous computational resources to use.
Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features
While a popular limit of infinite-width neural networks, the Neural Tangent Kernel (NTK) often exhibits performance gaps from finite-width neural networks on standard datasets, due to lack of feature learning.
