{"slug": "teller-dual-path-iterative-preference-optimization-for-table-entity-linking", "title": "TELLER: Dual-Path Iterative Preference Optimization for Table Entity Linking", "summary": "Researchers introduced TELLER, a dual-path iterative preference optimization method for table entity linking, which improves accuracy on the TableInstruct entity-linking subset from 94.35% to 94.50% via a direct-answer path and from 92.90% to 92.95% via a reasoning path, and on MammoTab V2 from 87.59% to 88.20% and from 79.09% to 81.85%, respectively. The method, detailed in arXiv:2607.28680v1, addresses static training supervision by refreshing preference data with residual errors and applying length-normalized regularized preference optimization.", "body_md": "arXiv:2607.28680v1 Announce Type: new\nAbstract: Entity linking in tables matches short and ambiguous cell mentions to their corresponding knowledge-base entities. Existing approaches typically rely on data preprocessing pipelines that retain either compact or extensive table content as contextual evidence, and then formulate entity linking as a language generation task for instruction-tuned models; recent systems further incorporate explicit reasoning to disambiguate challenging mentions. However, their training supervision is usually static: fixed preference data cannot adapt to the residual errors of an evolving model, while variations in reasoning length can bias sequence-level preference learning. To address these limitations, we present TELLER: Table Entity Linking through Learning from Errors and Reasoning. We first retrieve and rank Wikidata candidates and retain reduced table evidence in the prompt. The direct-answer path applies iterative direct preference optimization and refreshes its preference data with residual errors from the updated model. The reasoning path uses filtered and compressed chain-of-thought rationales for supervised fine-tuning, followed by our iterative length-normalized regularized preference optimization. On the TableInstruct entity-linking subset, the direct-answer path improves accuracy from 94.35\\% to 94.50\\%; on the MammoTab V2 evaluation set, it improves accuracy from 87.59\\% to 88.20\\%. The reasoning path improves accuracy from 92.90\\% to 92.95\\% on TableInstruct and from 79.09\\% to 81.85\\% on MammoTab V2, while maintaining high rates of complete reasoning generation. These results show that iterative preference learning benefits both concise entity prediction and explicit reasoning.", "url": "https://wpnews.pro/news/teller-dual-path-iterative-preference-optimization-for-table-entity-linking", "canonical_source": "https://arxiv.org/abs/2607.28680", "published_at": "2026-08-03 04:00:00+00:00", "updated_at": "2026-08-03 04:04:31.720738+00:00", "lang": "en", "topics": ["machine-learning", "natural-language-processing", "large-language-models"], "entities": ["TELLER", "Wikidata", "TableInstruct", "MammoTab V2", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/teller-dual-path-iterative-preference-optimization-for-table-entity-linking", "markdown": "https://wpnews.pro/news/teller-dual-path-iterative-preference-optimization-for-table-entity-linking.md", "text": "https://wpnews.pro/news/teller-dual-path-iterative-preference-optimization-for-table-entity-linking.txt", "jsonld": "https://wpnews.pro/news/teller-dual-path-iterative-preference-optimization-for-table-entity-linking.jsonld"}}