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1,505 claims from 935 papers are on the record. 46 have been checked so far; the other 1,459 have no check with a result yet.

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Status: Unchecked ×Keyword: fine-tuning ×Clear all

12 claims from 8 papers

1. Psychology › Philosophy and Theoretical Science

   Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics

   Kaugeranna, Kaugeranna and 4.6) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2023

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   1. Unchecked“Cosmological Reinterpretation: Quasi-Periodic Eruptions (QPEs) at galactic centers are reframed as rhythmic dimensional portal cycles, with the Big Bang as the maximum QPE: a higher-dimensional export of tuned constants into 3D reality, resolving fine-tuning…
   2. Unchecked“The portal density equation: [ F_d = \rho_{d+1} e^{-\Delta E / kT_{obs}} ] links civilizational consciousness growth to discovery rates, while informational black holes emerge in high-density DIT sessions, exceeding an informational Schwarzschild threshold […

2. Computer Science › Topic Modeling

   Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

   池谷, Tinn, Cheng et al. · ACM Transactions on Computing for Healthcare · 2021

   The paper compiles a biomedical NLP benchmark and reports that language models pretrained from scratch on biomedical text reach new state-of-the-art results across a wide range of tasks.

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   1. UncheckedFor fields with plenty of unlabelled text, like biomedicine, training language models from scratch gave substantial gains over adapting general-domain models.“In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.”
   2. UncheckedWith BERT models, some common practices, such as complex tagging schemes for named entity recognition, are found to be unnecessary.“Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition.”

3. Computer Science › Topic Modeling

   Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

   池谷, Tinn, Cheng et al. · ACM Transactions on Computing for Healthcare · 2021

   The paper argues that biomedical language models pretrained from scratch on domain text outperform continually pretrained general models, and releases a benchmark (BLURB), models and a leaderboard.

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   1. UncheckedFor fields with plenty of unlabelled text, such as biomedicine, training a language model from scratch gives substantial gains over adapting a general-domain model.“In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.”

4. Computer Science › Advanced Neural Network Applications

   Pruning Convolutional Neural Networks for Resource Efficient Inference

   Molchanov, Tyree, Karras, Aila and Kautz · arXiv (Cornell University) · 2016

   The paper proposes a Taylor-expansion criterion for pruning convolutional kernels, interleaved with fine-tuning, and tests it on transfer learning, a gesture classifier and ImageNet.

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   1. UncheckedA new Taylor-expansion pruning criterion is reported to beat weight-norm and activation criteria when pruning large CNNs adapted to Birds-200 and Flowers-102.“The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient informat…”

5. Computer Science › Advanced Neural Network Applications

   Rethinking the Value of Network Pruning

   Liu, Sun, Zhou, Huang and Darrell · arXiv (Cornell University) · 2018

   The paper tests common pruning pipelines and reports that training the pruned architecture from scratch matches or beats fine-tuning, suggesting the architecture matters more than inherited weights.

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   1. UncheckedIn the structured pruning methods examined, fine-tuning a pruned network performed no better than training the same small network from random starting weights.“For all state-of-the-art structured pruning algorithms we examined, fine-tuning a pruned model only gives comparable or worse performance than training that model with randomly initialized weights.”

6. Social Sciences › Misinformation and Its Impacts

   TruthfulQA: Measuring How Models Mimic Human Falsehoods

   Lin, Hilton and Evans · arXiv (Cornell University) · 2021

   The authors built a benchmark of 817 questions to test whether language models give truthful answers, and found that models fell well short of humans and that the largest were generally the least truthful.

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   1. UncheckedOn the TruthfulQA benchmark, the best language model tested gave truthful answers to 58% of questions, against 94% for human performance.“The best model was truthful on 58% of questions, while human performance was 94%.”

7. Computer Science › Advanced Neural Network Applications

   NISP: Pruning Networks using Neuron Importance Score Propagation

   Yu, Li, Chen et al. · arXiv (Cornell University) · 2017

   The paper proposes NISP, which scores each neuron's importance in the final response layer and propagates it backwards to prune the whole CNN, reporting large speed-ups and compression with negligible accuracy loss.

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   1. UncheckedThe authors argue that pruning a whole CNN jointly, to preserve important responses in the second-to-last layer, is essential for the pruned network to regain accuracy.“In contrast, we argue that it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the "final response layer" (FRL), which is the second-to-last layer before classification, for a pruned network to…”
   2. UncheckedThe paper ranks neurons in the network's second-to-last layer by importance and frames pruning earlier layers as an optimisation problem with a closed-form solution.“Specifically, we apply feature ranking techniques to measure the importance of each neuron in the FRL, and formulate network pruning as a binary integer optimization problem and derive a closed-form solution to it for pruning neurons in earlier layers.”

8. Medicine › Artificial Intelligence in Healthcare and Education

   Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline

   Li, Fu and Python · Journal of Medical Internet Research · 2025

   A review of 270 studies of large language models in clinical use mapped which models were used for which tasks, and proposed an interactive online guideline to help clinicians choose a suitable one.

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   1. Unchecked“GPT-3.5 and GPT-4 were the most versatile models in the 5-stage clinical workflow, applied to 52% (29/56) and 71% (40/56) of the clinical subtasks, respectively, and they performed best in 29% (16/56) and 54% (30/56) of the clinical subtasks, respectively.”
   2. UncheckedThis review found no evidence of a single general-purpose clinical language model that works well across a wide range of clinical tasks.“However, we did not find evidence of generalist clinical LLMs successfully applicable to a wide range of clinical tasks.”