6 papers
Supplement Generation Training for Enhancing Agentic Task Performance
Young Min Cho, Daniele Bonadiman, Divya Bhargavi +8
Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid obsolescence as new models are…
CONFETTI: Conversational Function-Calling Evaluation Through Turn-Level Interactions
Tamer Alkhouli, Katerina Margatina, James Gung +4
We introduce Conversational Function-Calling Evaluation Through Turn-Level Interactions (CONFETTI), a conversational benchmark1 designed to evaluate the function-calling capabiliti…
Eliciting Better Multilingual Structured Reasoning from LLMs through Code
Bryan Li, Tamer Alkhouli, Daniele Bonadiman +2
The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we…
Neural Simultaneous Speech Translation Using Alignment-Based Chunking
Patrick Wilken, Tamer Alkhouli, Evgeny Matusov +1
In simultaneous machine translation, the objective is to determine when to produce a partial translation given a continuous stream of source words, with a trade-off between latency…
On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation
Tamer Alkhouli, Gabriel Bretschner, Hermann Ney
This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in tran…
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
Albert Zeyer, Tamer Alkhouli, Hermann Ney
We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder. We sh…