Q-DAPS estimates question difficulty by computing the entropy of plausibility scores, excelling on four QA datasets.

Jamshid Mozafari, Bhawna Piryani, Adam Jatowt

MedHopQA evaluates biomedical QA via multi-hop reasoning with 1,000 expert-curated question-answer pairs.

Rezarta Islamaj, Robert Leaman, Joey Chan et al.

Sentiment and emotion classification of Indonesian e-commerce reviews using Multi-Task BiLSTM and AutoML, achieving high accuracy.

Hermawan Manurung, Ibrahim Al-Kahfi, Ahmad Rizqi et al.

SeaEvo enhances algorithm discovery via strategy space evolution, achieving 21% improvement in system optimization tasks.

Sichun Luo, Yi Huang, Haochen Luo et al.

Improving tabular retrieval robustness via representational stability using centroid averaging to reduce format-specific variance.

Kushal Raj Bhandari, Adarsh Singh, Jianxi Gao et al.

Study reveals representational harms in LLM narratives against Global Majority nationalities using a QA model on 500,000 stories.

Ilana Nguyen, Harini Suresh, Thema Monroe-White et al.

CRAFT method enhances BLEU score by 2.13 points in English-Hindi translation through clustered regression for adaptive filtering.

Parthasarathi Panda, Asheswari Swain, Subhrakanta Panda

BERAG improves retrieval-augmented generation with Bayesian ensemble, significantly enhancing knowledge-based visual question answering performance.

Jinghong Chen, Jingbiao Mei, Guangyu Yang et al.

EVENT5Ws: A large dataset for open-domain event extraction, manually annotated and statistically verified.

Praval Sharma, Ashok Samal, Leen-Kiat Soh et al.

Discovering a shared logical subspace in LLMs improves logical reasoning accuracy by up to 11% via alignment of natural-language and symbolic views.

Feihao Fang, My T. Thai, Yuanyuan Lei

InsightGen generates diverse and relevant insights to enhance open-ended document QA.

Saransh Sharma, Pritika Ramu, Aparna Garimella et al.

GPT models predict experience ratings from open-ended survey text; prompt optimization improves accuracy by 2%.

Andrew Hong, Jason Potteiger, Luis E. Zapata

Micro Language Models (μLMs) enable instant responses by generating the first 4-8 words on-device, with cloud models completing the response.

Wen Cheng, Tuochao Chen, Karim Helwani et al.

Study shows large language models impact AI conference peer reviews, especially in linguistic complexity and evaluative focus.

Wenqing Wu, Chengzhi Zhang, Yi Zhao et al.

The study reveals dual alignment between language model layers and human sentence processing, with early layers suited for natural reading and later layers better modeling complex syntactic processing.

Tatsuki Kuribayashi, Alex Warstadt, Yohei Oseki et al.

GSQ achieves high-accuracy low-bit quantization using Gumbel-Softmax sampling, narrowing the accuracy gap with QTIP methods.

Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan et al.

Transition-matrix regularization improves next dialogue act prediction in counseling conversations, boosting macro-F1 by 9-42%.

Eric Rudolph, Philipp Steigerwald, Jens Albrecht

ArbGraph enhances long-form RAG reliability through conflict-aware evidence arbitration, reducing hallucinations.

Qingying Niu, Yuhao Wang, Ruiyang Ren et al.

A dual-aspect evaluation framework analyzes LLMs on Vietnamese legal text, revealing readability-accuracy trade-offs.

Van-Truong Le

BAGEL benchmark evaluates language models' performance on animal knowledge using closed-book questions on taxonomy, morphology, etc.

Jiacheng Shen, Masato Hagiwara, Milad Alizadeh et al.