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104 results for “Hallucinations”
Structure conditioned hallucinated CDR sequences
<p><em><strong>Datasets accompanying publication https://doi.org/10.1101/2022.06.06.494991</strong></em> (Published in Frontiers in Immunology). Please refer to the final version of the manuscript on Frontiers.</p> <p><strong>RAbD dataset (Figure 2 in preprint/publication)</strong>: Structure conditioned hallucinated sequences for all 6 CDR loops of 60 antibodies (see publication above for more details) with wildtype seeding (runs_wtseed) and without wildtype seeding (runs_noseed). Full sequences are under runs_<>/<benchamark_name>/<cdr>/results/sequences.fasta.</p> <p><strong>DeepAb Testset (SI Table 1, SI Figure 4 in preprint/publication)</strong>: Structure conditioned hallucinated sequences for all 6 CDR loops of 20 antibodies selected from the DeepAb test set (see publication above for more details) with wildtype seeding (runs_wtseed) and without wildtype seeding (runs_noseed). Full sequences are under runs_<>/<benchamark_name>/<cdr>/results/sequences.fasta.</p> <p><strong>Trastuzumab hallucination in various modes described in the manuscript.</strong></p> <p>1. <strong>Unrestricted hallucination</strong>: In <strong>"unrestricted.tar"</strong>. Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>2. <strong>Motif-restricted hallucination (positions 95, 100A on heavy chain)</strong>: In "<strong>res2pos_95and100A.tar"</strong>. Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>3. <strong>Motif-restricted hallucination (positions 99, 100A on heavy chain)</strong>: In "<strong>res2pos_99and100A.tar</strong>". Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>4. <strong>Motif-restricted hallucination (positions 95, 99, 100, 100A on heavy chain)</strong>: In "<strong>res4pos.tar</strong>". Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>5. <strong>Motif-restricted (positions 99, 100A on heavy chain) and sequence-restricted (restricted to wildtype sequence) hallucination</strong>: In "<strong>res2pos_95and100A_and_sequence.tar</strong>". Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>For reproducing data, refer to code and methods in the preprint/published version.</p>
SemEval-2024 Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes
<p><strong>Task description:</strong> SHROOM participants will need to detect grammatically sound output that contains incorrect semantic information (i.e. unsupported or inconsistent with the source input), with or without having access to the model that produced the output.</p> <p><strong>Overview of the task:</strong> The modern NLG landscape is plagued by two interlinked problems: On the one hand, our current neural models have a propensity to produce inaccurate but fluent outputs; on the other hand, our metrics are most apt at describing fluency, rather than correctness. This leads neural networks to “hallucinate”, i.e., produce fluent but incorrect outputs that we currently struggle to detect automatically. For many NLG applications, the correctness of an output is however mission critical. For instance, producing a plausible-sounding translation that is inconsistent with the source text puts in jeopardy the usefulness of a machine translation pipeline. With our shared task, we hope to foster the growing interest in this topic in the community.</p> <p>With SHROOM we adopt a post hoc setting, where models have already been trained and outputs already produced: participants will be asked to perform binary classification to identify cases of fluent overgeneration hallucinations in two different setups: model-aware and model-agnostic tracks. That is, participants must detect grammatically sound outputs which contain incorrect or unsupported semantic information, inconsistent with the source input, with or without having access to the model that produced the output. To that end, we will provide participants with a collection of checkpoints, inputs, references and outputs of systems covering three different NLG tasks: definition modeling (DM), machine translation (MT) and paraphrase generation (PG), trained with varying degrees of accuracy. The development set will provide binary annotations from at least five different annotators and a majority vote gold label.</p>
Data for: Network of autoscopic hallucinations elicited by intracerebral stimulations of periventricular nodular heterotopia: an SEEG study
<p>Periventricular nodular heterotopias (PVNH) are areas of neurons abnormally located in the white matter that might be involved in physiological cortical functions. Autoscopic hallucinations are changes in self-consciousness determined by a mismatch in integration of multiple sensory inputs. Our goal is to highlight the brain network involved in generation of autoscopic hallucination elicited by electrical stimulation of a PVNH in a drug resistant epilepsy patient.</p> <p> Our patient was explored using stereo-electroencephalography with electrodes covering the right posterior temporal PVNH and the adjacent cortex. Direct electrical high frequency stimulation of the PVNH elicited autoscopic hallucinations mainly involving the face and upper trunk. We then used multiple modalities to determine brain connectivity: single pulse electrical stimulation of the PVNH and stimulation-evoked potentials were used to highlight resting state effective connectivity. High-frequency stimulation using alternating polarity pulses enabled us to identify the network involved, time-locked to the clinical effect and to map symptom-related effective connectivity. Functional connectivity using a non-linear regression method was used to determine dependencies between different cortical regions following the stimulation. Finally, structural connectivity was highlighted using deterministic fiber tracking.</p> <p>Multi-modal connectivity analysis identified a network involving the PVNH, occipital and temporal neocortex, fusiform gyrus and parietal cortex.</p>
Data for: Network of autoscopic hallucinations elicited by intracerebral stimulations of periventricular nodular heterotopia: an SEEG study
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Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code
<p>Despite their success, large language models (LLMs) face the critical challenge of hallucinations, generating plausible but incorrect content. While much research has focused on hallucinations in multiple modalities including images and natural language text, less attention has been given to hallucinations in source code, which leads to incorrect and vulnerable code that causes significant financial loss. To pave the way for research in LLMs' hallucinations in code, we introduce Collu-Bench, a benchmark for predicting code hallucinations of LLMs across code generation (CG) and automated program repair (APR) tasks. Collu-Bench includes 13,234 code hallucination instances collected from five datasets and 11 diverse LLMs, ranging from open-source models to commercial ones. <br>To better understand and predict code hallucinations, Collu-Bench provides detailed features such as the per-step log probabilities of LLMs' output, token types, and the execution feedback of LLMs' generated code for in-depth analysis. In addition, we conduct experiments to predict hallucination on Collu-Bench, using both traditional machine learning techniques and neural networks, which achieves 22.03 -- 33.15% accuracy. Our experiments draw insightful findings of code hallucination patterns, reveal the challenge of accurately localizing LLMs' hallucinations, and highlight the need for more sophisticated techniques.</p>
STimulation to Improve Auditory haLLucinations
ClinicalTrials.gov study NCT02360228. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Imagery Interventions for Auditory Vocal Hallucinations
ClinicalTrials.gov study NCT05603260. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
TDCS for Auditory Hallucinations in Schizophrenia
ClinicalTrials.gov study NCT01898299. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Transcranial Current Stimulation as a Treatment for Auditory Hallucinations in Schizophrenia
ClinicalTrials.gov study NCT01963676. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Transcranial Direct Current Stimulation (TDCS) for Auditory Hallucinations in Early Onset Schizophrenia (EOS)
ClinicalTrials.gov study NCT02764164. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Models of Auditory Hallucination
ClinicalTrials.gov study NCT04210557. IPD Sharing: NO. Countries: 1. Publications: 54.
Feasibility Electrical Stimulation Study for Visual Hallucinations
ClinicalTrials.gov study NCT04870710. IPD Sharing: NO. Countries: 1. Publications: 1.
Targeting Auditory Hallucinations With Alternating Current Stimulation
ClinicalTrials.gov study NCT03221270. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Use of Transcranial Electrical Stimulation for Hallucinations
ClinicalTrials.gov study NCT02715765. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Bilateral Repetitive Transcranial Magnetic Stimulation for Auditory Hallucinations Results
ClinicalTrials.gov study NCT04548622. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Fig. 1 in Multiple Visual Hallucinations and Pseudohallucinations in One Individual Patient: When the World Is Turning Upside Down and the Television Keeps Falling to the Ground while Dwarfs Are Parading on the Ceiling
<p>Fig. 1. coronary (c) T 2 - weighted magnetic resonance images clearly demonstrating predominantly small-vessel disease with lacunar lesions in the right pons (arrows in a and c) and bilaterally in the subcortical insular cortex and to a lesser extent in the periventricular white matter. The size of the ventricles and the extent of atrophy do not exceed age-related normal values.</p>
Fig. 1 Visual hallucinations observed during auditory-visual synaesthesia. a First episode, b Second episode, c in Auditory-visual synaesthesia in a patient with basilia migraine
<p>Fig. 1 Visual hallucinations observed during auditory-visual synaesthesia. a First episode, b Second episode, c Third episode.</p>
Fig. 1 in Multiple Visual Hallucinations and Pseudohallucinations in One Individual Patient: When the World Is Turning Upside Down and the Television Keeps Falling to the Ground while Dwarfs Are Parading on the Ceiling
<p>Fig. 1. Axial (a, b) T 2 - weighted magnetic resonance images clearly demonstrating predominantly small-vessel disease with lacunar lesions in the right pons (arrows in a and c) and bilaterally in the subcortical insular cortex and to a lesser extent in the periventricular white matter. The size of the ventricles and the extent of atrophy do not exceed age-related normal values.</p>
rTMS for Auditory Hallucinations Guided by Magnetoencephalography
ClinicalTrials.gov study NCT05598450. IPD Sharing: NO. Countries: 1. Publications: 2.
Transcranial Magnetic Stimulation (TMS) for Patients With Treatment Resistant Auditory Verbal Hallucination
ClinicalTrials.gov study NCT03762746. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.