2026· SemEval@ACL· pp. 2057-2064· 1 citation· 29 references
Computer Science
TL;DR
This system leverages LLMs both for extracting high-level aspects and to encode them with state-of-the-art generalist embedding models, and applies a series of embedding post-processing steps to fit the embedding space with a Mahalanobis-like diagonal metric.
Abstract
The Narrative Story Similarity and Narrative Representation Learning (NSNRL) task measures the narrative similarity between two stories based on three core aspects: the abstract theme, the course of action, and the outcomes. Our system leverages LLMs both for extracting high-level aspects and to encode them with state-of-the-art generalist embedding models. We then apply a series of embedding post-processing steps and learn to fit the embedding space with a Mahalanobis-like diagonal metric. We show that some of these techniques should not be applied universally, as they do not necessarily increase performance or overfit, depending on the base encoder. Our system outperforms the baseline only in Track B, ranking twelfth out of twenty-seven on the final leaderboard, while performing lower than the baseline accuracy in Track A.
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Across OLMo-2, Llama-3.1, and Qwen-3, under both MEMIT and AlphaEdit and in batch and sequential regimes, Moir consistently extends preservation in the most vulnerable domains, suggesting that aligning the preservation distribution with the model's operative distribution is a key factor in non-destructive editing and t...
Jea Kwon, Jiwon Kim, Dong-Kyum Kim et al.· arXiv.org· 0 citations
TF-IDF provides a strong no-training lexical retrieval baseline, alongside representation analyses and generation controls for the learned models, and one narrow result remains: the decoder-only model prefers authentic word order in 107 of 112 minimal-pair comparisons, but does not reproduce held-out commentary content...
Temporal reasoning benchmarks for Video-LLMs are often mediated by language, leaving room for linguistic shortcuts from option wording, answer correlations, or language priors, so TempCloze is introduced, a video cloze benchmark for evaluating visual temporal reasoning in Video-LLMs.
Wen-Qi Pei, Henry Hengyuan Zhao, Yi-Lai Liu et al.· 0 citations
Text-compatible JEPA objectives must preserve multiple plausible completions rather than compress them into a single latent point, showing that text-compatible JEPA objectives must preserve multiple plausible completions rather than compress them into a single latent point.
LSR retains a general encoder as a transferable semantic substrate while explicit modules reorganize, augment, or index its representations for a defined operational objective.
Pedro Emílio Amador Salomão· Nexus Science Review· 0 citations
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