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Priya Gupta
A dataset for testing the effectiveness of unlearning methods in large language models. -
Negative preference optimization: From catastrophic collapse to effective unl...
A dataset for testing the effectiveness of unlearning methods in large language models. -
JailbreakZoo
The JailbreakZoo dataset contains a collection of jailbreak prompts and responses for large language models and vision-language models. -
CMR Scaling Law
The dataset used in the paper is a mixture of general corpus and domain-specific corpus, with a power-law relationship between loss, mixture ratio, and training tokens scale. -
Fairness Certification for Natural Language Processing and Large Language Models
The dataset used in the paper is a large corpus of text data, which is used to train and evaluate natural language processing models. -
Integer or floating point? new outlooks for low-bit quantization on large lan...
The dataset used in the paper is not explicitly described, but it is mentioned that it is a large language model dataset. -
A comprehensive study on post-training quantization for large language models
The ZeroQuant dataset is a large language model dataset used in the paper. -
Opt: Open pre-trained transformer language models
The OPT dataset is a large language model dataset used in the paper. -
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Fl...
The dataset used in the paper is not explicitly described, but it is mentioned that it is a large language model dataset. -
WebWISE: Web Interface Control and Sequential Exploration with Large Language...
The paper investigates using a Large Language Model (LLM) to automatically perform web software tasks using click, scroll, and text input operations. -
Learning to summarize with human feedback
The paper presents a study on the impact of synthetic data on large language models (LLMs) and proposes a method to steer LLMs towards desirable non-differentiable attributes. -
MACHIAVELLI Benchmark
A dataset of traces from the MACHIAVELLI environment, including API calls and their outcomes. -
BELLS: A Framework Towards Future Proof Benchmarks for the Evaluation of LLM ...
A structured collection of tests for input-output safeguards, including established failure tests, emerging failure tests, and next-gen architecture tests. -
Synthetic Workload for LLM Serving
The dataset used in the paper is a synthetic workload, where clients send requests with different input and output lengths, and with varying request rates. -
LLM Ethics Dataset
The dataset used in this study to explore the ethical issues surrounding Large Language Models (LLMs).