Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.

The electronic state spectroscopy of acetone (CH3)2CO has been investigated using high-resolution VUV photoabsorption spectroscopy in the energy range 3.7-10.8 eV. New vibronic structure has been observed, notably in the low energy absorption band assigned to the 1(1)A(1) --> 1(1)A2 (ny --> pi*) transition. The local absorption maximum at 7.85 eV has been tentatively attributed to the 4(1)A1 (pi --> pi*) transition. Six Rydberg series converging to the lowest ionisation energy (9.708 eV) have been assigned as well as a newly-resolved ns Rydberg series converging to the first ionic excited state (12.590 eV). Rydberg orbitals of each series have been classified according to the magnitude of the quantum defect (delta) and are extended to higher quantum numbers than in the previous analyses.

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Image segmentation has become an important tool in orthopedic and biomechanical research. However, it greatly remains a time-consuming and laborious task. In this manuscript, we propose a fully automatic model-based segmentation pipeline for the full lower limb in computed tomography (CT) images. The method relies on prior shape model fitting, followed by a gradientdefined free from deformation. The technique allows for the generation of anatomically corresponding surface meshes, which can subsequently be applied in anatomical and mechanical simulation studies. Starting from an initial, small (n ≤ 10) sample of manual segmentations, the model is continuously updated and refined with newly segmented training samples. Validation of the segmentation pipeline was performed by comparing the automatic segmentations against corresponding manual segmentations. Convergence of the segmentation pipeline was obtained in 250 cases and failed in three samples. The average distance error ranged from 0.53 to 0.76 mm and maximal error ranged from 2.0 to 7.8 mm for the 7 different osteological structures that were investigated. The accuracy of the shape model-based segmentation gradually increased as the number of training shapes in the updated population also increased. When optimized with the free form deformation, however, average segmentation accuracy rapidly plateaued from already as little as 20 training samples on. The maximum segmentation error plateaued from 100 training samples on.

Ion-pair formation has been studied in hyperthermal (30-100 eV) neutral potassium collisions with gas phase thymine (C(5)H(6)N(2)O(2)) and uracil (C(4)H(4)N(2)O(2)). Negative ions formed by electron transfer from the alkali atom to the target molecule were analysed by time-of-flight (TOF) mass spectrometry. The most abundant product anions are assigned to CNO(-) and (U-H)(-)/(T-H)(-) and the associated electron transfer mechanisms are discussed. Special emphasis is given to the enhancement of ring breaking pathways in the present experiments, notably CNO(-) formation, compared with free electron attachment measurements.

Peripheral nerve injury remains a clinical challenge with severe physiological and functional consequences. Despite the existence of multiple possible therapeutic approaches, until now, there is no consensus regarding the advantages of each option or the best methodology in promoting nerve regeneration. Regenerative medicine is a promise to overcome this medical limitation, and in this work, chitosan nerve guide conduits and olfactory mucosa mesenchymal stem/stromal cells were applied in different therapeutic combinations to promote regeneration in sciatic nerves after neurotmesis injury. Over 20 weeks, the intervened animals were subjected to a regular functional assessment (determination of motor performance, nociception, and sciatic indexes), and after this period, they were evaluated kinematically and the sciatic nerves and cranial tibial muscles were evaluated stereologically and histomorphometrically, respectively. The results obtained allowed confirming the beneficial effects of using these therapeutic approaches. The use of chitosan NGCs and cells resulted in better motor performance, better sciatic indexes, and lower gait dysfunction after 20 weeks. The use of only NGGs demonstrated better nociceptive recoveries. The stereological evaluation of the sciatic nerve revealed identical values in the different parameters for all therapeutic groups. In the muscle histomorphometric evaluation, the groups treated with NGCs and cells showed results close to those of the group that received traditional sutures, the one with the best final values. The therapeutic combinations studied show promising outcomes and should be the target of new future works to overcome some irregularities found in the results and establish the combination of nerve guidance conduits and olfactory mucosa mesenchymal stem/stromal cells as viable options in the treatment of peripheral nerves after injury.

We present negative ion formation from collisions of neutral potassium atoms with D-ribose (C 5 H 10 O 5 ), the sugar unit in the DNA/RNA molecule. From the negative ion time-of-flight (TOF) mass spectra, OH − is the main fragment detected in the collision range 50-100 eV accounting on average for 50% of the total anion yield. Prominence is also given to the rich fragmentation pattern observed with special attention to O − (16 m/z) formation. These results are in sharp contrast to dissociative electron attachment experiments. The TOF mass spectra assignments show that these channels are also observed, albeit with a much lower relative intensity. Branching ratios of the most abundant fragment anions as a function of the collision energy are obtained, allowing to establish a rationale on the collision dynamics. © 2013 AIP Publishing LLC. [http://dx