RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Claim: Hence, the disparity in decodability between color and digit attributes becomes significantly more pronounced in a 5-layer MLP in comparison to a 3-layer MLP.
Context: As observed in Fig. 2(b) , the initial phases of training for both networks emphasize color attribute (since bias is easy to learn), resulting in a notable enhancement in the decodability of color in both models. Also, as the training progresses, the decodability of the digit is higher in a 3-layer model when compared to a 5-layer model.
Caption: (a); (b); Exploring the Effect of depth modulation: (a) illustrates how the linear decodability of features decreases as neural network depth increases, while (b) dives into the training dynamics of MLPs with varying depths under ERM.
Claim: The performance improvement is particularly noteworthy when dealing with large tables.
Context: Figure 3 shows the comparison results of ALTER and methods following the pre-LLM era, including CABINET and OMNITAB, partitioning tables in the WikiTQ dataset by the number of cells. In Table 4 , we present the results based on different table sizes divided by the token count in the WikiTQ dataset, comparing our method with Dater, Chain-of-TABLE, and Binder unique to the LLM era. Table 4 shows that ALTER significantly outperforms all three methods in the LLM era across different table sizes.
Caption: Table 4: Comparison of methods in the LLM era with tables divided by token count on WikiTQ. ( underline denotes the second-best performance; bold denotes the best performance)