Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Discovering Latent Knowledge in Language Models Without Supervision
Authors: Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | evaluated across 6 models and 10 question-answering datasets, CCS outperforms the accuracy of strong zero-shot baselines by 4% on average (Section 3.2.1). We systematically analyze CCS to understand the features it discovers. |
| Researcher Affiliation | Academia | Collin Burns UC Berkeley Haotian Ye Peking University Dan Klein UC Berkeley Jacob Steinhardt UC Berkeley |
| Pseudocode | Yes | Algorithm 1 Pseudocode for Getting Contrast Features |
| Open Source Code | Yes | We provide code at https://www.github.com/collin-burns/discovering_latent_knowledge. |
| Open Datasets | Yes | We test models on 10 datasets: sentiment classification (IMDB (Maas et al., 2011) and Amazon (Mc Auley & Leskovec, 2013)), topic classification (AG-News (Zhang et al., 2015) and DBpedia-14 (Lehmann et al., 2015)), NLI (RTE (Wang et al., 2018) and QNLI (Rajpurkar et al., 2016)), story completion (COPA (Roemmele et al., 2011) and Story-Cloze (Mostafazadeh et al., 2017)), question answering (Bool Q (Clark et al., 2019)), and common sense reasoning (PIQA (Bisk et al., 2020)). |
| Dataset Splits | No | randomly split each dataset into an unsupervised training set (60% of the data) and test set (40%). The paper does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running experiments. |
| Software Dependencies | No | The paper mentions 'Huggingface library' and 'Adam optimizer' but does not provide specific version numbers for these software dependencies or other key libraries like Python or PyTorch/TensorFlow. |
| Experiment Setup | Yes | When testing CCS, we optimize it 10 times using Adam W (Loshchilov & Hutter, 2017) with learning rate 0.01, then take the run with the lowest unsupervised loss. ... In practice, we train each time for E = 1000 epochs with a learning rate η = 0.01 (which we found was good for consistently achieving low unsupervised loss) in each run. |