Advancements in machine learning, particularly Large Language Models (LLMs), offer new opportunities for enhancing education through personalized assistance. We introduce “Be My Mate,” an agent that leverages LLMs to simulate virtual peer students in online collaborative education. The system includes a subscription module for real-time updates and a conversational module for generating supportive interactions. Key challenges include creating temporally realistic interactions and credible error generation. The initial demonstration shows promise in enhancing student engagement and learning outcomes.

We introduce MTSwitch, a web-based system for the bidirectional translation between molecules and texts, leveraging various large language models (LLMs). It supports two crucial tasks, including molecule captioning (explaining the properties of a molecule) and molecule generation (designing a molecule based on specific properties). To the best of our knowledge, MTSwitch is currently the first accessible system that allows users to translate between molecular representations and descriptive text contents. The system and a screencast can be found in https://github.com/hanninaa/MTSwitch.

Recent advancements have led to the adaptation of several multimodal large language models (LLMs) for critical video-related use cases, particularly in Video Question-Answering (QA). However, most of the previous models sample only a limited number of frames from video due to the context size limit of backbone LLM. Another approach of applying temporal pooling to compress multiple frames, is also shown to saturate and does not scale well. These limitations cause videoQA on long videos to perform very poorly. To address this, we present VideoRAG, a system to utilize recently popularized Retrieval Augmented Generation (RAG) pipeline to select the top-k frames from video, relevant to the user query. We have observed a qualitative improvement in our experiments, indicating a promising direction to pursue. Additionally, our findings indicate that videoRAG demonstrates superior performance when addressing needle-in-the-haystack questions in long videos. Our extensible system allows for trying multiple strategies for indexing, ranking, and adding QA models.

Four years on from two papers (Belz et al., 2020; Howcroft et al., 2020) that first called out the lack of standardisation and comparability in the quality criteria assessed in NLP system evaluations, researchers still use widely differing quality criteria names and definitions, meaning that it continues to be unclear when the same aspect of quality is being assessed in two evaluations. While normalised quality criteria were proposed at the time, the list was unwieldy and using it came with a steep learning curve. In this demo paper, our aim is to address these issues with an interactive taxonomy tool that enables quick perusal and selection of the quality criteria, and provides decision support and examples of use at each node.

We present ‘factgenie‘: a framework for annotating and visualizing word spans in textual model outputs. Annotations can capture various span-based phenomena such as semantic inaccuracies or irrelevant text. With ‘factgenie‘, the annotations can be collected both from human crowdworkers and large language models. Our framework consists of a web interface for data visualization and gathering text annotations, powered by an easily extensible codebase.

Wikipedia is known to have systematic gaps in its coverage that correspond to under-resourced languages as well as underrepresented groups. This paper presents a new tool to support efforts to fill in these gaps by automatically generating draft articles and facilitating post-editing and uploading to Wikipedia. A rule-based generator and an input-constrained LLM are used to generate two alternative articles, enabling the often more fluent, but error-prone, LLM-generated article to be content-checked against the more reliable, but less fluent, rule-generated article.