Two papers by DFKI-NLP authors accepted to EMNLP 2026

Two papers from researchers in the DFKI-NLP group have been accepted as Main papers at the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026). EMNLP will take place October 24 –29 in Budapest, Hungary. The first paper, titled “Training-Free Character-Length Control in Summarization with Diffusion Language Models”, introduces a training-free inference-time method for masked diffusion LMs that estimates expected character length at each denoising step and dynamically inserts or removes masked positions to steer toward a target length. The proposed approach requires no fine-tuning and applies to any off-the-shelf masked diffusion LM. Across three summarization benchmarks, it reduces character-length errors by an order of magnitude compared to baselines while maintaining competitive summary quality. The second paper analyses evaluation trends from 2020 to 2025 across 4 major NLP conferences, in particular the adoption of LLM-as-a-judge in comparison to human evaluation. Based on an automatic and human-verified approach to extracting evaluation approaches, metrics and criteria from more than 3300 NLG papers, it identifies systemic evaluation challenges and proposes an evaluation checklist to guide metric selection, construct validity and LLM-as-a-judge deployment.