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Found 18 papers, 15 papers with code
A Feasibility Study of Answer-Unaware Question Generation for Education
We conduct a feasibility study into the applicability of answer-unaware question generation models to textbook passages.
The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank
While previous work has tackled this problem with models trained specifically to do fill in the blank, a more useful model is one that can effectively perform _both_ FitB and continuation tasks.
Group-Adaptive Threshold Optimization for Robust AI-Generated Text Detection
The advancement of large language models (LLMs) has made it difficult to differentiate human-written text from AI-generated text.
GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text Detection Challenge
In this shared task, using the newly released RAID benchmark, we aim to answer whether or not models can detect generated text from a large, yet fixed, number of domains and LLMs, all of which are seen during training.
MiRAGeNews: Multimodal Realistic AI-Generated News Detection
To combat the spread of AI-generated fake news, we propose the MiRAGeNews Dataset, a dataset of 12, 500 high-quality real and AI-generated image-caption pairs from state-of-the-art generators.
ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems
Recently, there has been increasing interest in using Large Language Models (LLMs) to construct complex multi-agent systems to perform tasks such as compiling literature reviews, drafting consumer reports, and planning vacations.
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
Many commercial and open-source models claim to detect machine-generated text with extremely high accuracy (99% or more).
FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models
One type of question that is commonly found in day-to-day scenarios is ``fan-out'' questions, complex multi-hop, multi-document reasoning questions that require finding information about a large number of entities.
Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck
Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability.
Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applications
Language model applications are becoming increasingly popular and complex, often including features like tool usage and retrieval augmentation.
Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models
Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given.
Simultaneous Speech-to-Speech Translation
Speech-to-Speech Translation
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Exploring the Curious Case of Code Prompts
Furthermore, we show that the style of code prompt has a large effect on performance for some but not all tasks and that fine-tuning on text instructions leads to better relative performance of code prompts.
Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text
As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
The Case for a Single Model that can Both Generate Continuations and Fill in the Blank
The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text.
A Feasibility Study of Answer-Agnostic Question Generation for Education
We conduct a feasibility study into the applicability of answer-agnostic question generation models to textbook passages.
RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text
In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text.
Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power.
