Long Form Question Answering

ELI5 is a dataset for long-form question answering. It contains 270K complex, diverse questions that require explanatory multi-sentence answers. Web search results are used as evidence documents to answer each question. ELI5 is also a task in Dodecadialogue. Source: ELI5 Image Source: https://arxiv.org/pdf/1907.09190v1.pdf

QuALITY (Question Answering with Long Input Texts, Yes!) is a multiple-choice question answering dataset for long document comprehension. The dataset consists of context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can process. Unlike in prior work with passages, the questions are written and validated by contributors who have read the entire passage, rather than relying on summaries or excerpts.

LLeQA is a French native dataset for studying information retrieval and long-form question answering in the legal domain. It consists of a knowledge corpus of 27,941 statutory articles collected from the Belgian legislation, and 1,868 legal questions posed by Belgian citizens and labeled by experienced jurists with a comprehensive answer rooted in relevant articles from the corpus.

Daily Activity Recordings for Artificial Intelligence (DARai, pronounced "Dahr-ree") is a multimodal, hierarchically annotated dataset constructed to understand human activities in real-world settings. DARai consists of continuous scripted and unscripted recordings of 50 participants in 10 different environments, totaling over 200 hours of data from 20 sensors including multiple camera views, depth and radar sensors, wearable inertial measurement units (IMUs), electromyography (EMG), insole pressure sensors, biomonitor sensors, and gaze tracker. To capture the complexity in human activities, DARai is annotated at three levels of hierarchy: (i) high-level activities (L1) that are independent tasks, (ii) lower-level actions (L2) that are patterns shared between activities, and (iii) fine-grained procedures (L3) that detail the exact execution steps for actions. The dataset annotations and recordings are designed so that 22.7% of L2 actions are shared between L1 activities and 14.2% of L3 procedures are shared between L2 actions. The overlap and unscripted nature of DARai allows counterfactual activities in the dataset. Experiments with various machine learning models showcase the value of DARai in uncovering important challenges in human-centered applications. Specifically, we conduct unimodal and multimodal sensor fusion experiments for recognition, temporal localization, and future action anticipation across all hierarchical annotation levels. To highlight the limitations of individual sensors, we also conduct domain-variant experiments that are enabled by DARai's multi-sensor and counterfactual activity design setup. The code, documentation, and dataset are available at the dedicated DARai website.

Vision-language supervised fine-tuning effectively enhances VLLM performance, but existing visual instruction tuning datasets have limitations: 1. Instruction Annotation Quality: Despite strong performance, advanced VLLMs may generate instructions with inaccuracies, such as hallucinations. 2. Instruction and Image Diversity: Limited instruction types and lack of diverse image data impact the model's ability to generate varied and realistic outputs. MMInstruct Dataset To address these challenges, we created the MMInstruct dataset, featuring: - 973K instructions from 24 domains - Four instruction types: Judgement, Multiple-Choice, Long Visual Question Answering, and Short Visual Question Answering.