GPT-3 shows that scaling an autoregressive language model to 175 billion parameters enables strong few-shot performance across diverse NLP tasks via in-context prompting without fine-tuning.
hub
arXiv preprint arXiv:1606.06031 , year=
17 Pith papers cite this work. Polarity classification is still indexing.
17
Pith papers citing it
abstract
We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. We show that LAMBADA exemplifies a wide range of linguistic phenomena, and that none of several state-of-the-art language models reaches accuracy above 1% on this novel benchmark. We thus propose LAMBADA as a challenging test set, meant to encourage the development of new models capable of genuine understanding of broad context in natural language text.
hub tools
representative citing papers
KVM is a novel block-recurrent compressed memory for attention that unifies expandable transformer context with linear RNN efficiency, enabling competitive long-context performance with released code and models.
MIXAR is the first autoregressive pixel-based language model for eight languages and scripts, with empirical gains on multilingual tasks, robustness to unseen languages, and further improvements when scaled to 0.5B parameters.
GPTQ quantizes 175B-parameter GPT models to 3-4 bits per weight in one shot using approximate second-order information, achieving negligible accuracy degradation and 3-4x inference speedups.
Intra-layer model parallelism in PyTorch enables training of 8.3B-parameter transformers, achieving SOTA perplexity of 10.8 on WikiText103 and 66.5% accuracy on LAMBADA.
Structured Recurrent Mixers enable algebraic switching between parallel training and recurrent inference representations, delivering higher efficiency, information capacity, and throughput than other linear-complexity models.
MoHGE achieves standard MoE performance with 20% fewer parameters and balanced GPU utilization via grouped heterogeneous experts, two-level routing, and specialized auxiliary losses.
Strix delivers sub-microsecond fault localisation, detection, and correction on NPUs with 1.04x slowdown and minimal hardware cost by system-level re-partitioning and targeted safeguards.
A two-stage distillation recipe converts a Pythia-1B Transformer into a Mamba model that preserves performance with perplexity 14.11 versus the teacher's 13.86.
DeepSpeed-Ulysses keeps communication volume constant for sequence-parallel attention when sequence length and device count scale together, delivering 2.5x faster training on 4x longer sequences than prior SOTA.
PaLM 540B demonstrates continued scaling benefits by setting new few-shot SOTA results on hundreds of benchmarks and outperforming humans on BIG-bench.
Ranked preference modeling outperforms imitation learning for language model alignment and scales more favorably with model size.
Toeplitz MLP Mixers replace attention with masked Toeplitz multiplications for sub-quadratic complexity while retaining more sequence information and outperforming on copying and in-context tasks.
ADAPT is an online reweighting framework for LLM training that outperforms offline data selection and mixing methods in cross-benchmark generalization under equal compute.
An aging-aware adaptive voltage scaling framework for AI accelerators reduces predicted threshold voltage shifts by ~19% and aging degradation by up to 46% while saving 14% lifetime power by leveraging neural network resilience.
Gemma introduces open 2B and 7B LLMs derived from Gemini technology that beat comparable open models on 11 of 18 text tasks and come with safety assessments.
Gemma 2 models achieve leading performance at their sizes by combining established Transformer modifications with knowledge distillation for the 2B and 9B variants.
citing papers explorer
-
Strix: Re-thinking NPU Reliability from a System Perspective
Strix delivers sub-microsecond fault localisation, detection, and correction on NPUs with 1.04x slowdown and minimal hardware cost by system-level re-partitioning and targeted safeguards.
-
Aging Aware Adaptive Voltage Scaling for Reliable and Efficient AI Accelerators
An aging-aware adaptive voltage scaling framework for AI accelerators reduces predicted threshold voltage shifts by ~19% and aging degradation by up to 46% while saving 14% lifetime power by leveraging neural network resilience.