Search Results for author:
Found 9 papers, 2 papers with code
Core: Robust Factual Precision Scoring with Informative Sub-Claim Identification
Hallucinations -- the generation of untrue claims -- pose a challenge to the application of large language models (LLMs) [1] thereby motivating the development of metrics to evaluate factual precision.
A Survey of Video Datasets for Grounded Event Understanding
In this paper, we survey 105 video datasets that require event understanding capability, consider how they contribute to the study of robust event understanding in video, and assess proposed video event extraction tasks in the context of this body of research.
On the Evaluation of Machine-Generated Reports
Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users.
Tur[k]ingBench: A Challenge Benchmark for Web Agents
To support the evaluation of TurkingBench, we have developed a framework that links chatbot responses to actions on web pages (e. g., modifying a text box, selecting a radio button).
TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning
It is challenging to perform question-answering over complex, multimodal content such as television clips.
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic.
MultiVENT: Multilingual Videos of Events with Aligned Natural Text
Everyday news coverage has shifted from traditional broadcasts towards a wide range of presentation formats such as first-hand, unedited video footage.
Ambiguous Images With Human Judgments for Robust Visual Event Classification
However, humans are frequently presented with visual data that they cannot classify with 100% certainty, and models trained on standard vision benchmarks achieve low performance when evaluated on this data.
Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce.
