arXiv Machine Learning
Aug 27

The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure

The paper critiques a recent NLI benchmark that tests the imperfective paradox, arguing that the benchmark suffers from conceptual and evaluation mis-specifications, notably Aspectual Reduction and a lack of strict NLI standards. The authors re-evaluate the benchmark, identify mis-specifications, and construct lexically matched minimal pairs to control for lexical variation. Their experiments reveal that models often exhibit a Sufficiency Bias, accept simple‑past hypotheses without affirming culmination, and that prompting interventions shift label decisions without improving true semantic understanding, highlighting additional failure modes such as compositional aspectual classification errors and surface‑form attraction.

By Kaiqiao Han, Yizhou Sun
arXiv Computation and Language
Aug 31

Diverging Transformer Predictions for Human Sentence Processing: A Comprehensive Analysis of Agreement Attraction Effects

The study evaluates eleven autoregressive transformer models on English agreement attraction scenarios using a surprisal-based approach. Results show that while transformers match human reading times for prepositional phrase configurations, they perform poorly on object‑extracted relative clauses, with predictions diverging across models and failing to capture human interference patterns. The authors argue that current transformers cannot adequately model human morphosyntactic processing and call for more rigorous, comprehensive testing to avoid misleading conclusions from limited syntactic setups.

By Titus von der Malsburg, Sebastian Pad\'o
arXiv AI
3d ago

Look Before You Leap: Factual Decoding with Internal Attribution Signals

The paper introduces DescaPE, a decoding framework that uses internal model signals to reduce hallucinations in large language models. By identifying a factual‑salient layer span and training a lightweight probe to approximate its signal, DescaPE penalizes high‑risk continuations and rewards factually grounded ones during inference. Experiments on five factuality benchmarks across three LLMs show that DescaPE improves factuality with only a 1.10× latency overhead.

By Hayeong Ryu, JungMin Yun, Byeonggeuk Lim, Sunhee Jo, YoungBin Kim
arXiv Computation and Language
Sep 10

SWORD: Wikidata-based Distortions Reveal Hidden Cross-Lingual Inconsistencies in LLM Factual Error Rejection

SWORD is a new benchmark that tests large language models’ ability to reject factually incorrect statements across eight major languages by distorting Wikidata triples. The benchmark reveals that models often perform better on semantically plausible distortions than on random ones, indicating a reliance on distributional familiarity rather than true factual verification. It also shows significant performance drops for East Asian languages, with gaps up to 28 percentage points, highlighting asymmetric multilingual factual reasoning capabilities.

By Sanghyeok Park, Minji Kang, Hosung Kwak, Jinhyuk Yun