103 citations · 330 across the 70 of their papers we have counts for
6 papers · 2 filters
The Role of Disfluencies in Speech Translation
Maike Züfle, Maria Teleki, Fabian Retkowski +5
Current speech translation systems, including SpeechLLMs, are trained on cleaned text and tend to strip disfluencies like filled pauses and false starts rather than translate them.…
Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation
Xingyu Su, Jacob Helwig, Shubham Parashar +6
We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs). Rather than pretraining from scratch, prior work replaces the causal attention i…
DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints
Zhitong Chen, Kai Yin, Weifeng Zhang +7
Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools, from satellite analysis to flood prediction and damage assessment, into coherent mu…
Learnability-Informed Fine-Tuning of Diffusion Language Models
Shubham Parashar, Atharv Chagi, Jacob Helwig +5
We aim to improve the reasoning capabilities of diffusion language models (DLMs). While SFT is a popular post-training recipe for autoregressive models, its use in DLMs faces chall…
Beyond Single Ground Truth: Reference Monism as Epistemic Injustice in ASR Evaluation
Anna Seo Gyeong Choi, Maria Teleki, James Caverlee +3
Automatic speech recognition (ASR) evaluation compares system output to ground truth transcripts, with Word Error Rate (WER) quantifying the distance between them. But ground truth…
DisastQA: A Comprehensive Benchmark for Evaluating Question Answering in Disaster Management
Zhitong Chen, Kai Yin, Xiangjue Dong +7
Accurate question answering (QA) in disaster management requires reasoning over uncertain and conflicting information, a setting poorly captured by existing benchmarks built on cle…