270 research outputs found
Theory Solving Made Easy with Clingo 5
Answer Set Programming (ASP) is a model, ground, and solve paradigm. The integration of application- or theory-specific reasoning into ASP systems thus impacts on many if not all elements of its workflow, viz. input language, grounding, intermediate language, solving, and output format. We address this challenge with the fifth generation of the ASP system clingo and its grounding and solving components by equipping them with well-defined generic interfaces facilitating the manifold integration efforts. On the grounder\u27s side, we introduce a generic way of specifying language extensions and propose an intermediate format accommodating their ground representation. At the solver end, this is accompanied by high-level interfaces easing the integration of theory propagators dealing with these extensions
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
We expose a surprising failure of generalization in auto-regressive large
language models (LLMs). If a model is trained on a sentence of the form "A is
B", it will not automatically generalize to the reverse direction "B is A".
This is the Reversal Curse. For instance, if a model is trained on "Olaf Scholz
was the ninth Chancellor of Germany", it will not automatically be able to
answer the question, "Who was the ninth Chancellor of Germany?". Moreover, the
likelihood of the correct answer ("Olaf Scholz") will not be higher than for a
random name. Thus, models exhibit a basic failure of logical deduction and do
not generalize a prevalent pattern in their training set (i.e. if "A is B''
occurs, "B is A" is more likely to occur). We provide evidence for the Reversal
Curse by finetuning GPT-3 and Llama-1 on fictitious statements such as "Uriah
Hawthorne is the composer of 'Abyssal Melodies'" and showing that they fail to
correctly answer "Who composed 'Abyssal Melodies?'". The Reversal Curse is
robust across model sizes and model families and is not alleviated by data
augmentation. We also evaluate ChatGPT (GPT-3.5 and GPT-4) on questions about
real-world celebrities, such as "Who is Tom Cruise's mother? [A: Mary Lee
Pfeiffer]" and the reverse "Who is Mary Lee Pfeiffer's son?". GPT-4 correctly
answers questions like the former 79% of the time, compared to 33% for the
latter. This shows a failure of logical deduction that we hypothesize is caused
by the Reversal Curse. Code is available at
https://github.com/lukasberglund/reversal_curse.Comment: 18 pages, 10 figure
Taken out of context: On measuring situational awareness in LLMs
We aim to better understand the emergence of `situational awareness' in large
language models (LLMs). A model is situationally aware if it's aware that it's
a model and can recognize whether it's currently in testing or deployment.
Today's LLMs are tested for safety and alignment before they are deployed. An
LLM could exploit situational awareness to achieve a high score on safety
tests, while taking harmful actions after deployment. Situational awareness may
emerge unexpectedly as a byproduct of model scaling. One way to better foresee
this emergence is to run scaling experiments on abilities necessary for
situational awareness. As such an ability, we propose `out-of-context
reasoning' (in contrast to in-context learning). We study out-of-context
reasoning experimentally. First, we finetune an LLM on a description of a test
while providing no examples or demonstrations. At test time, we assess whether
the model can pass the test. To our surprise, we find that LLMs succeed on this
out-of-context reasoning task. Their success is sensitive to the training setup
and only works when we apply data augmentation. For both GPT-3 and LLaMA-1,
performance improves with model size. These findings offer a foundation for
further empirical study, towards predicting and potentially controlling the
emergence of situational awareness in LLMs. Code is available at:
https://github.com/AsaCooperStickland/situational-awareness-evals
Attenuation Correction Using Template PET Registration for Brain PET:A Proof-of-Concept Study
NeuroLF is a dedicated brain PET system with an octagonal prism shape housed in a scanner head that can be positioned around a patient’s head. Because it does not have MR or CT capabilities, attenuation correction based on an estimation of the attenuation map is a crucial feature. In this article, we demonstrate this method on [18F]FDG PET brain scans performed with a low-resolution proof of concept prototype of NeuroLF called BPET. We perform an affine registration of a template PET scan to the uncorrected emission image, and then apply the resulting transform to the corresponding template attenuation map. Using a whole-body PET/CT system as reference, we quantitively show that this method yields comparable image quality (0.893 average correlation to reference scan) to using the reference µ-map as obtained from the CT scan of the imaged patient (0.908 average correlation). We conclude from this initial study that attenuation correction using template registration instead of a patient CT delivers similar results and is an option for patients undergoing brain PET.</p
Attenuation Correction Using Template PET Registration for Brain PET: A Proof-of-Concept Study
NeuroLF is a dedicated brain PET system with an octagonal prism shape housed in a scanner head that can be positioned around a patient's head. Because it does not have MR or CT capabilities, attenuation correction based on an estimation of the attenuation map is a crucial feature. In this article, we demonstrate this method on [18F]FDG PET brain scans performed with a low-resolution proof of concept prototype of NeuroLF called BPET. We perform an affine registration of a template PET scan to the uncorrected emission image, and then apply the resulting transform to the corresponding template attenuation map. Using a whole-body PET/CT system as reference, we quantitively show that this method yields comparable image quality (0.893 average correlation to reference scan) to using the reference µ-map as obtained from the CT scan of the imaged patient (0.908 average correlation). We conclude from this initial study that attenuation correction using template registration instead of a patient CT delivers similar results and is an option for patients undergoing brain PET.
Keywords: Nifty-Reg; PET; STIR; attenuation correction; brain; image reconstruction; registration; tomography
Attenuation Correction Using Template PET Registration for Brain PET:A Proof-of-Concept Study
NeuroLF is a dedicated brain PET system with an octagonal prism shape housed in a scanner head that can be positioned around a patient’s head. Because it does not have MR or CT capabilities, attenuation correction based on an estimation of the attenuation map is a crucial feature. In this article, we demonstrate this method on [18F]FDG PET brain scans performed with a low-resolution proof of concept prototype of NeuroLF called BPET. We perform an affine registration of a template PET scan to the uncorrected emission image, and then apply the resulting transform to the corresponding template attenuation map. Using a whole-body PET/CT system as reference, we quantitively show that this method yields comparable image quality (0.893 average correlation to reference scan) to using the reference µ-map as obtained from the CT scan of the imaged patient (0.908 average correlation). We conclude from this initial study that attenuation correction using template registration instead of a patient CT delivers similar results and is an option for patients undergoing brain PET.</p
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