RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Claim: From Table 4 , we observe that removing low-correlated heuristic RAG features, actually helps the learning to rank model to learn better leading to a conclusion that not necessarily all features are important.
Evidence type: table
Caption: Table 4: Kendall Tau ( \tau ) scores using different features for training the random forest regression model.
Context: Are all heuristic features necessary? We experiment with the set of features used for learning to rank model training as a surrogate judge. We evaluate four training configurations: (i) all features (ii) without LLM-measured features (iii) without language detection and support, i.e., the low-correlation features observed in Figure 5 , and (iv) including only LLM-measured features.
Verdict: Supported
Claim: Next, removing the LLM-measured features completely or only keeping them decreases the Kendall-Tau ( \tau ) correlation score in \mirage .
Evidence type: table
Caption: Table 4: Kendall Tau ( \tau ) scores using different features for training the random forest regression model.
Context: Are all heuristic features necessary? We experiment with the set of features used for learning to rank model training as a surrogate judge. We evaluate four training configurations: (i) all features (ii) without LLM-measured features (iii) without language detection and support, i.e., the low-correlation features observed in Figure 5 , and (iv) including only LLM-measured features. From Table 4 , we observe that removing low-correlated heuristic RAG features, actually helps the learning to rank model to learn better leading to a conclusion that not necessarily all features are important.
Verdict: Supported