## Heatmap: Dataset Transfer Performance ### Overview The image is a heatmap visualizing the transfer performance between different datasets. The rows represent the training dataset, and the columns represent the test dataset. The color intensity indicates the performance, with redder colors indicating positive transfer and bluer colors indicating negative transfer. Numerical values are overlaid on each cell, providing precise performance metrics. ### Components/Axes * **X-axis (Test dataset):** TriviaQA, HotpotQA, Movies, Winobias, Winogrande, NLI, IMDB, Math, HotpotQA\_WC, NQ\_WC * **Y-axis (Train dataset):** TriviaQA, HotpotQA, Movies, Winobias, Winogrande, NLI, IMDB, Math, HotpotQA\_WC, NQ\_WC * **Colorbar (right side):** Ranges from -0.2 (blue) to 0.3 (red), with intermediate values of -0.1, 0.0, 0.1, 0.2. This represents the transfer performance score. ### Detailed Analysis or Content Details The heatmap displays a matrix of transfer performance values. Each cell (i, j) represents the performance of a model trained on dataset i (row) and tested on dataset j (column). Here's a breakdown of the values: * **TriviaQA:** * Trained on TriviaQA: 0.11 * Trained on HotpotQA: -0.05 * Trained on Movies: 0.04 * Trained on Winobias: -0.04 * Trained on Winogrande: -0.04 * Trained on NLI: 0.01 * Trained on IMDB: -0.19 * Trained on Math: 0.10 * Trained on HotpotQA\_WC: -0.08 * Trained on NQ\_WC: 0.02 * **HotpotQA:** * Trained on TriviaQA: -0.05 * Trained on HotpotQA: 0.08 * Trained on Movies: 0.04 * Trained on Winobias: 0.04 * Trained on Winogrande: 0.02 * Trained on NLI: -0.03 * Trained on IMDB: -0.03 * Trained on Math: -0.01 * Trained on HotpotQA\_WC: -0.04 * Trained on NQ\_WC: -0.05 * **Movies:** * Trained on TriviaQA: -0.01 * Trained on HotpotQA: -0.08 * Trained on Movies: 0.08 * Trained on Winobias: -0.08 * Trained on Winogrande: -0.09 * Trained on NLI: -0.08 * Trained on IMDB: -0.06 * Trained on Math: -0.03 * Trained on HotpotQA\_WC: -0.10 * Trained on NQ\_WC: 0.02 * **Winobias:** * Trained on TriviaQA: -0.21 * Trained on HotpotQA: -0.18 * Trained on Movies: -0.22 * Trained on Winobias: 0.33 * Trained on Winogrande: 0.12 * Trained on NLI: 0.02 * Trained on IMDB: 0.04 * Trained on Math: -0.19 * Trained on HotpotQA\_WC: -0.16 * Trained on NQ\_WC: -0.07 * **Winogrande:** * Trained on TriviaQA: -0.15 * Trained on HotpotQA: -0.17 * Trained on Movies: -0.17 * Trained on Winobias: 0.02 * Trained on Winogrande: 0.23 * Trained on NLI: 0.04 * Trained on IMDB: -0.16 * Trained on Math: -0.10 * Trained on HotpotQA\_WC: -0.16 * Trained on NQ\_WC: -0.13 * **NLI:** * Trained on TriviaQA: -0.24 * Trained on HotpotQA: -0.21 * Trained on Movies: -0.19 * Trained on Winobias: -0.03 * Trained on Winogrande: 0.05 * Trained on NLI: 0.32 * Trained on IMDB: -0.21 * Trained on Math: -0.07 * Trained on HotpotQA\_WC: -0.16 * Trained on NQ\_WC: -0.15 * **IMDB:** * Trained on TriviaQA: -0.12 * Trained on HotpotQA: -0.23 * Trained on Movies: -0.08 * Trained on Winobias: 0.04 * Trained on Winogrande: 0.01 * Trained on NLI: 0.04 * Trained on IMDB: 0.10 * Trained on Math: -0.04 * Trained on HotpotQA\_WC: -0.16 * Trained on NQ\_WC: -0.10 * **Math:** * Trained on TriviaQA: -0.19 * Trained on HotpotQA: -0.22 * Trained on Movies: -0.14 * Trained on Winobias: -0.02 * Trained on Winogrande: -0.10 * Trained on NLI: 0.02 * Trained on IMDB: 0.04 * Trained on Math: 0.22 * Trained on HotpotQA\_WC: -0.13 * Trained on NQ\_WC: -0.18 * **HotpotQA\_WC:** * Trained on TriviaQA: -0.10 * Trained on HotpotQA: -0.03 * Trained on Movies: -0.19 * Trained on Winobias: -0.04 * Trained on Winogrande: -0.11 * Trained on NLI: -0.11 * Trained on IMDB: 0.05 * Trained on Math: -0.00 * Trained on HotpotQA\_WC: 0.08 * Trained on NQ\_WC: -0.02 * **NQ\_WC:** * Trained on TriviaQA: -0.07 * Trained on HotpotQA: -0.11 * Trained on Movies: -0.07 * Trained on Winobias: -0.04 * Trained on Winogrande: 0.06 * Trained on NLI: -0.03 * Trained on IMDB: 0.07 * Trained on Math: -0.19 * Trained on HotpotQA\_WC: -0.14 * Trained on NQ\_WC: 0.18 ### Key Observations * The diagonal elements (training and testing on the same dataset) generally show positive transfer, as indicated by the redder colors and positive values. * Negative transfer (blue colors and negative values) is observed in many off-diagonal elements, suggesting that training on one dataset can sometimes hurt performance on another. * Winobias and NLI datasets show strong positive transfer when trained and tested on themselves (0.33 and 0.32 respectively). * Training on TriviaQA often results in negative transfer to other datasets, as seen by the blueish colors in the first row. ### Interpretation The heatmap provides insights into the transferability of knowledge between different datasets. The positive diagonal elements indicate that models generally perform best when trained and tested on the same dataset. The off-diagonal elements reveal how well a model trained on one dataset generalizes to another. Negative transfer highlights potential domain differences or biases that prevent effective generalization. The data suggests that some datasets are more similar and benefit from transfer learning, while others are distinct and require specific training. For example, training on Winobias or NLI seems to provide a strong positive transfer when tested on the same dataset, suggesting these datasets have unique characteristics that are well-learned by the models. Conversely, training on TriviaQA often leads to negative transfer, indicating that the knowledge gained from TriviaQA may not be directly applicable or may even be detrimental to performance on other datasets.