A methodical investigation of temporal prompting techniques for LLM-based EL is presented, and it is demonstrated that explicit temporal prompting can reduce drift mistakes by up to 40% using a dataset of temporally-sensitive mentions linked with Wikidata snapshots.
A new benchmark, KIFI, is designed, which comprises 1032 carefully selected instances from the TRUE and ScreenEval datasets, with key information annotated, and it is shown that LLMs frequently fail to use the appropriate information to make correct decisions.
Xindi Guo, Zhen Xie, Patrick H. Chen· Annual International ACM SIG...· 0 citations
This work presents the first systematic study of inverse relation directionality in LLMs, using a benchmark consisting of 5,457 instances spanning 27 distinct inverse relation labels and reveals systematic asymmetries in inverse relation classification across LLMs.
This framework proposes a context-editing framework that performs selective abstraction over entities that appear in both the context and the question, establishing symbolic abstraction as a highly cost-efficient solution for ensuring context fidelity in LLMs.
Rounak Sharma, Debabrata Mahapatra, S. Saini· Annual International ACM SIG...· 0 citations
A formal and comprehensive taxonomy for classifying natural language queries along four dimensions, designed as a foundation for diagnostic evaluation of QA and IR systems by providing a systematic categorization of the temporal properties of queries.
Ivano Lauriola· International Conference on...· 0 citations
This tutorial provides a structured and in-depth overview of the complete temporal information access pipeline: Temporal Information Extraction (TIE), Temporal Information Retrieval (TIR), and Temporal Question Answering (TQA).
Bhawna Piryani, Avishek Anand, Omar Alonso et al.· Annual International ACM SIG...· 0 citations
This work introduces PRISM Edit, which optimizes a single polysemous representation across temporal contexts and leverages the model's inherent modulation pathway to route it to temporally correct predictions without requiring any architectural modification.
