31 p. LLM Post-Training: A Deep Dive into Reasoning Large Language Models 10 p. TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction 18 p. Topological Quantum Dark Matter via Global Anomaly Cancellation 21 p. How far can we go with ImageNet for Text...
TruthfulQA主要就是针对"Imitative Falsehoods"(模仿性谎言)问题构建的测试集。 2、数据集 简介:817条数据,跨38个类别。由作者构建的具有对抗性的问题(人类认为模型易错的问题),大部分问题都是一句话,约为9个单词。 数据集位置:https://github.com/sylinrl/TruthfulQA/blob/main/TruthfulQA.csv 目标:在于引出大...
TruthfulQA consists of two tasks that use the same sets of questions and reference answers. Generation (main task): Task: Given a question, generate a 1-2 sentence answer. Objective: The primary objective is overall truthfulness, expressed as the percentage of the model's answers that are tru...
3. TruthfulQA 4. Experiments 5. Results 6. Discussion & Conclusion 在Meta提出的LLAMA-1[1]中,研究人员在第五节中讨论了LLAMA中的Bias, Toxicity and Misinformation,在其中主要谈到了三个有关Harmless的部分。包括WinoGender,RealToxicityPrompts,CrowS-Pairs这三个部分。研究人员根据这三个成熟的数据集,对LLAMA...
TruthfulQA 基准要求模型根据给定的问题提供真实、准确和可靠的答案。模型不仅需要关注答案是否正确,还需要考虑答案是否具有真实性和可信度。为了完成这个任务,模型需要具备以下能力: 1.理解问题: 模型需要理解问题并能够确定问题所需回答的类型。 2.知识获取: 模型需要从各种来源(如文本、数据库等)中获取相关知识。 3...
TruthfulQA 基准主要由以下几个部分组成: (1) 数据集:数据集是 TruthfulQA 基准的核心部分,通常包含大量的问题和对应的答案。这些问题和答案通常是从互联网、书籍、文章等不同来源中抽取的。 (2) 评估指标:评估指标是衡量机器学习模型性能的重要依据。TruthfulQA 基准通常采用准确率、召回率、F1 值等指标来评估模型...
README Apache-2.0 license truthfulqa-multi Multilingual TruthfulQA Obtain the answers of the model using harness: sbatch experiments/generative.slurm Judge the answers with the judge-model: sbatch judge/run_experiments/judge.slurm Evaluate the judges: python judge/correlate_to_manual.py About...
GPT‑2 and a T5-based model. The best model was truthful on 58% of questions, while human performance was 94%. Models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. The largest models were generally the least truthful. This contrasts ...
Law allowing recovery for truthful defamatory statement violated First Amendment where statement related to public concern
TruthfulQA基准是一个用来衡量语言模型在生成答案到问题时是否真实的测试集。该基准测试集包含817个问题,模型的主要任务是给定prompt和question,生成完整的句子回复,给定答案集合,计算每个问题下选择正确回复的可能性之和。 在TruthfulQA基准测试中,Llama2的表现非常出色,这表明它的回答更加真实、安全、可靠。该基准测试集...