arXiv AI
Oct 9

Structure Tax: How Structured Output affects LLMs Performance

The paper investigates the so‑called ‘structure tax’ that is often attributed to constraining large language model outputs to structured formats like JSON or XML. By evaluating various models, datasets, and schema designs, the authors find that the accuracy loss depends more on how the schema is organized than on the presence of structure itself. Specifically, reasoning‑first field ordering can match or surpass free‑form accuracy, whereas answer‑first ordering leads to significant drops, especially in smaller models. The study also shows that well‑designed schemas improve confidence calibration and the separability of correct versus incorrect representations in transformer layers.

By Vineet Kumar, Kanishka, Bhuvanesh Mandora
arXiv AI
Aug 26

PARTAB: Partition-Aware Reasoning with Structured Evidence for Scalable Table Understanding

PARTAB is a framework that improves large language model reasoning on tables by constructing a structured evidence interface. It represents query‑relevant evidence as semantically coherent, row‑linked table regions and performs hierarchical selection over column groups and row‑level partitions before composing the evidence for answer generation. Evaluations on multiple table reasoning benchmarks show that PARTAB consistently outperforms full‑table prompting and recent methods, achieving strong performance on WikiTableQuestions and TabFact while remaining competitive on numerical reasoning tasks.

By Md Mahadi Hasan Nahid, Davood Rafiei
arXiv AI
Jul 9

Co-LMLM: Continuous-Query Limited Memory Language Models

arXiv:2607. 07707v1 Announce Type: cross Abstract: Limited memory language models (LMLMs) externalize factual knowledge during pretraining to a knowledge base (KB), rather than memorizing it in their weights.

By Yair Feldman, Linxi Zhao, Nathan Godey, Dongyoung Go, Yilun Hua, Kilian Q. Weinberger, Jennifer J. Sun, Yoav Artzi