prompts stringlengths 81 413 | metrics_response stringlengths 0 371 |
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What metrics were used to measure the Transformer-XL (RMS dynamic eval) model in the Dynamic Evaluation of Transformer Language Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the [?]-former (SM) model in the $\infty$-former: Infinite Memory Transformer paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the -former (SM) model in the $\infty$-former: Infinite Memory Transformer paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the ∞-former (Sticky memories + initialized GPT-2 Small) model in the $\infty$-former: Infinite Memory Transformer paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the ∞-former (initialized GPT-2 Small) model in the $\infty$-former: Infinite Memory Transformer paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Hybrid H3 (355M) model in the Hungry Hungry Hippos: Towards Language Modeling with State Space Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Transformer-XL (SGD dynamic eval) model in the Dynamic Evaluation of Transformer Language Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Compressive Transformer (18L, M=1024) model in the Compressive Transformers for Long-Range Sequence Modelling paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the SRU++ Large model in the When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the SegaTransformer-XL model in the Segatron: Segment-Aware Transformer for Language Modeling and Understanding paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Transformer+SSA+Self-ensemble model in the The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Transformer-XL Large + Phrase Induction model in the Improving Neural Language Models by Segmenting, Attending, and Predicting the Future paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the GPT-2 Full model in the Language Models are Unsupervised Multitask Learners paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Staged Training model in the Shortformer: Better Language Modeling using Shorter Inputs paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Transformer+SSA model in the The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Sandwich Transformer model in the Improving Transformer Models by Reordering their Sublayers paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the DIFFQ (λ=1, g=16) model in the Differentiable Model Compression via Pseudo Quantization Noise paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Mega model in the Mega: Moving Average Equipped Gated Attention paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Shortformer model in the Shortformer: Better Language Modeling using Shorter Inputs paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Feedback Transformer (8 layers) model in the Addressing Some Limitations of Transformers with Feedback Memory paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the SRU++ Base model in the When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Transformer-XL Large model in the Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the PAR Transformer Large model in the Pay Attention when Required paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Hyena-3-slim model in the Hyena Hierarchy: Towards Larger Convolutional Language Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Hyena-3 model in the Hyena Hierarchy: Towards Larger Convolutional Language Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Transformer (Adaptive inputs) model in the Adaptive Input Representations for Neural Language Modeling paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the T2R + Pretrain model in the Finetuning Pretrained Transformers into RNNs paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Subformer model in the Subformer: A Parameter Reduced Transformer paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the BERT-Large-CAS model in the Language Models with Transformers paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the All-attention network (36 layers) model in the Augmenting Self-attention with Persistent Memory paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the S4 model in the Efficiently Modeling Long Sequences with Structured State Spaces paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the GPT-2 Large model in the Language Models are Unsupervised Multitask Learners paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Feedback Transformer (4 layers) model in the Addressing Some Limitations of Transformers with Feedback Memory paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the PAR Transformer Base model in the Pay Attention when Required paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the DEQ-Transformer (medium, adaptive embed) model in the Deep Equilibrium Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the TaLK Convolutions model in the Time-aware Large Kernel Convolutions paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Rfa-Gate-Gaussian-Stateful (Big) model in the Random Feature Attention paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Hybrid H3 (125M) model in the Hungry Hungry Hippos: Towards Language Modeling with State Space Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Transformer-XL Standard model in the Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the DeLighT model in the DeLighT: Deep and Light-weight Transformer paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the [?]-former (Sticky memories) model in the $\infty$-former: Infinite Memory Transformer paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the \infty-former (Sticky memories) model in the $\infty$-former: Infinite Memory Transformer paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the ∞-former (Sticky memories) model in the $\infty$-former: Infinite Memory Transformer paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Transformer-N model in the Revisiting Simple Neural Probabilistic Language Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the FNetAR Medium model in the FNetAR: Mixing Tokens with Autoregressive Fourier Transforms paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the GPT-2 Medium model in the Language Models are Unsupervised Multitask Learners paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the AdvSoft (+ 4 layer QRNN + dynamic eval) model in the Improving Neural Language Modeling via Adversarial Training paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the DEQ-TrellisNet model in the Deep Equilibrium Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Trellis Network model in the Trellis Networks for Sequence Modeling paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the LSTM (Hebbian, Cache, MbPA) model in the Fast Parametric Learning with Activation Memorization paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the LSTM (Hebbian, Cache) model in the Fast Parametric Learning with Activation Memorization paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Rfa-Gate-Gaussian-Stateful (Small) model in the Random Feature Attention paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the LSTM (RMC) model in the Relational recurrent neural networks paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the DEQ-Transformer (small) model in the Deep Equilibrium Models paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the AWD-LSTM-MoS + ATOI model in the Alleviating Sequence Information Loss with Data Overlapping and Prime Batch Sizes paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the 4 layer QRNN model in the An Analysis of Neural Language Modeling at Multiple Scales paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the LSTM (Hebbian) model in the Fast Parametric Learning with Activation Memorization paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the LSTM model in the Fast Parametric Learning with Activation Memorization paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the GCNN-8 model in the Language Modeling with Gated Convolutional Networks paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the GPT-2 Small model in the Language Models are Unsupervised Multitask Learners paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Neural cache model (size = 2,000) model in the Improving Neural Language Models with a Continuous Cache paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Neural cache model (size = 100) model in the Improving Neural Language Models with a Continuous Cache paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the GCNN-8 model in the Language Modeling with Gated Convolutional Networks paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the TCN model in the An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Temporal CNN model in the Convolutional Sequence Modeling Revisited paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the LSTM model in the Improving Neural Language Models with a Continuous Cache paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Transformer (Adaptive inputs) model in the On the adequacy of untuned warmup for adaptive optimization paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the LSTM model in the How much complexity does an RNN architecture need to learn syntax-sensitive dependencies? paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the GRU model in the How much complexity does an RNN architecture need to learn syntax-sensitive dependencies? paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the Decay RNN model in the How much complexity does an RNN architecture need to learn syntax-sensitive dependencies? paper on the WikiText-103 dataset? | Test perplexity, Validation perplexity, Number of params |
What metrics were used to measure the I-DARTS model in the Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition paper on the PTB Diagnostic ECG Database dataset? | PPL |
What metrics were used to measure the Gopher model in the Scaling Language Models: Methods, Analysis & Insights from Training Gopher paper on the PubMed Central dataset? | BPB |
What metrics were used to measure the Gopher model in the Scaling Language Models: Methods, Analysis & Insights from Training Gopher paper on the OpenWebtext2 dataset? | BPB |
What metrics were used to measure the Gopher model in the Scaling Language Models: Methods, Analysis & Insights from Training Gopher paper on the StackExchange dataset? | BPB |
What metrics were used to measure the Gopher model in the Scaling Language Models: Methods, Analysis & Insights from Training Gopher paper on the PubMed Cognitive Control Abstracts dataset? | BPB |
What metrics were used to measure the GPT-2 (48 layers, h=1600) model in the Language Models are Unsupervised Multitask Learners paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer-XL (24 layers, RMS dynamic eval, decay) model in the Dynamic Evaluation of Transformer Language Models paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Focus model in the Focus Your Attention (with Adaptive IIR Filters) paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Expire-Span (24 layers) model in the Not All Memories are Created Equal: Learning to Forget by Expiring paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the SRU++ Large model in the When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Feedback Transformer model in the Addressing Some Limitations of Transformers with Feedback Memory paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Sandwich Transformer (adaptive span) model in the Improving Transformer Models by Reordering their Sublayers paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Compressive Transformer (24 layers) model in the Compressive Transformers for Long-Range Sequence Modelling paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer-LS (large) model in the Long-Short Transformer: Efficient Transformers for Language and Vision paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the SRU++ Base model in the When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer (24 layers, 8k adaptive span) model in the Adaptive Attention Span in Transformers paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer-XL (24 layers) model in the Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Longformer (30 layers, h=512) model in the Longformer: The Long-Document Transformer paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Sparse Transformer (30 layers, fixed attn) model in the Generating Long Sequences with Sparse Transformers paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Routing Transformer (12 layers) model in the Efficient Content-Based Sparse Attention with Routing Transformers paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer-LS (small) model in the Long-Short Transformer: Efficient Transformers for Language and Vision paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Hourglass model in the Hierarchical Transformers Are More Efficient Language Models paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Longformer (12 layers, h=512) model in the Longformer: The Long-Document Transformer paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the All-attention network (18 layers) model in the Augmenting Self-attention with Persistent Memory paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer (12 layers, 8k adaptive span) model in the Adaptive Attention Span in Transformers paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the BP-Transformer (12 layers) model in the BP-Transformer: Modelling Long-Range Context via Binary Partitioning paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer+SSA model in the The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer-XL (18 layers) model in the Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer (64 layers) model in the Character-Level Language Modeling with Deeper Self-Attention paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
What metrics were used to measure the Transformer-XL (12 layers) model in the Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context paper on the enwik8 dataset? | Bit per Character (BPC), Number of params |
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