7 accepted papers
The evolution of Large Language Models (LLMs) has sparked growing concerns about jailbreak, crafted prompts that bypass safety guardrails and lead to the generation of harmful information. While recent research primarily focuses on identifying harmful content within LLM outputs, limited attention ha…
Existing multi-hop question generation (QG) methods treat answer-irrelevant documents as non-essential and remove them as impurities. However, this approach can create a training-inference discrepancy when impurities cannot be completely removed, which can lead to a decrease in model performance. To…
In Spoken Question Answering (SQA), automatic speech recognition (ASR) outputs are often relayed to language models for QA. However, constructing such a cascaded framework with large language models (LLMs) in a real-time SQA setting involves realistic challenges, such as noise in the ASR output, the…
We present HyperSum, an extractive summarization framework that captures both the efficiency of traditional lexical summarization and the accuracy of contemporary neural approaches. HyperSum exploits the pseudo-orthogonality that emerges when randomly initializing vectors at extremely high dimension…
Text classification with extremely weak supervision (EWS) imposes stricter supervision constraints compared to regular weakly supervise classification. Absolutely no labeled training samples or hand-crafted rules specific to the evaluation data are allowed. Such restrictions limit state-of-the-art E…
Visual Question Answering (VQA) stands to benefit from the boost of increasingly sophisticated Pretrained Language Model (PLM) and Computer Vision-based models. In particular, many language modality studies have been conducted using image captioning or question generation with the knowledge ground o…
In weakly-supervised text classification, only label names act as sources of supervision. Predominant approaches to weakly-supervised text classification utilize a two-phase framework, where test samples are first assigned pseudo-labels and are then used to train a neural text classifier. In most pr…