{"arxiv_id":"2311.17780","incoming":{"papers":1,"sentences":1,"groups":[{"kind":"data_used","label":"Uses its data or values","papers":1,"sentences":1,"quotes":[{"arxiv_id":"2607.20487","title":"Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering","url":"/paper/2607.20487","section":"Data and Task","sentence":"We use the QBias corpus [ref] , which contains 21,747 U.S. political news articles collected from AllSides balanced news headline roundups (June 2012–November 2022) and labeled by expert annotators as left, center, or right.","intent_p":0.98,"contests_p":null}]}],"unlabelled_sentences":0},"outgoing":{"papers":1,"sentences":1,"groups":[{"kind":"method_or_tool","label":"Uses the method or tool of","papers":1,"sentences":1,"quotes":[{"arxiv_id":"1910.01108","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","url":"/paper/1910.01108","section":"Domain-Adopting DistilBERT","sentence":"With the goal of capturing biased language in pretrained language models and using the HuggingFace Transformers module [ref] , we fine-tune the base DistilBERT model, which was trained in Wikipedia articles and a large book dataset.[<https://huggingface.co/distilbert-base-uncased>] DistilBERT– [ref] was chosen as a base model since it has proven to perform well with comparably small datasets, performs better than BERT despite its smaller size [ref] , and for its aptness for adopting domain-specificity [ref] .","intent_p":0.97,"contests_p":null}]}],"unlabelled_sentences":0},"claims":[],"thresholds":{"intent_p":0.8,"supports_p":0.95,"contests_p":0.7},"method":"Citing sentences come from each paper's LaTeX source; every \\cite marks the sentence and the works it cites. Pith's classifier typed each sentence. A label is shown at intent probability 0.8 or more (0.95 for support), a contest at 0.7 or more. Measured accuracy against two blind model families is at https://pith.science/accuracy. The quoted sentences are the citing authors' own.","held_up":null,"_links":{"html":"https://pith.science/paper/2311.17780"}}