12 accepted papers
Previous fast–slow system architectures demonstrated that pairing a reactive E2E planner with a deliberative vision-language model (VLM) can address these long-tail scenarios. However, these dual-system models that query the slow module at fixed intervals are computationally inefficient and introduc…
Ensuring factual consistency in summarization remains a challenge, especially for long-document evaluation. While automated, reference-free evaluation models are essential given the impracticality of large-scale human assessment for lengthy texts, challenges persist in evaluating different systems o…
Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. To overcome this, we introduce AgentThink , a pioneering unified framewor
Detecting factual inconsistency for long document summarization remains challenging, given the complex structure of the source article and long summary length. In this work, we study factual inconsistency errors and connect them with a line of discourse analysis. We find that errors are more common…
This paper addresses the challenge of aspect-based summarization in education by introducing Reflective ASPect-based summarization (ReflectASP), a novel dataset that summarizes student reflections on STEM lectures. Despite the promising performance of large language models in general summarization,…
There are more than 300 documented signed languages worldwide, which are indispensable avenues for computational linguists to study cross-cultural and cross-linguistic factors that affect automatic sign understanding and generation. Yet, these are studied under critically low-resource settings, espe…
This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students’ reflective writing. The goal of ReflectSumm is to facilitate developing and evaluating novel summarization techniques tailored to real-world scenarios with little training data, with pote…
We propose a simple approach for the abstractive summarization of long legal opinions that takes into account the argument structure of the document. Legal opinions often contain complex and nuanced argumentation, making it challenging to generate a concise summary that accurately captures the main…
End-to-end sign language generation models do not accurately represent the prosody in sign language. A lack of temporal and spatial variations leads to poor-quality generated presentations that confuse human interpreters. In this paper, we aim to improve the prosody in generated sign languages by mo…
Biases continue to be prevalent in modern text and media, especially subjective bias – a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth. To tackle the problem of detecting and further mitigating subjective bias, we introduce a manuall…
This paper is about regularizing deep convolutional networks (CNNs) based on an adaptive framework for transfer learning with limited training data in the target domain. Recent advances of CNN regularization in this context are commonly due to the use of additional regularization objectives. They gu…
Highly accurate mapping and localization is of prime importance for mobile robotics, and its core lies in efficient scan matching. Previous research are focusing on designing a robust objective function and the residual error distribution is often ignored or simply assumed as unitary or mixture of s…