Large Language Model Alignment: A Survey

Recent years have witnessed remarkable progress made in large language models (LLMs). Such advancements, while garnering significant attention, have concurrently elicited various concerns. The potential of these models is undeniably vast; however, they may yield texts that are imprecise, misleading, or even detrimental. Consequently, it becomes paramount to employ alignment techniques to ensure these models to exhibit behaviors consistent with human values. This survey endeavors to furnish an extensive exploration of alignment methodologies designed for LLMs, in conjunction with the extant capability research in this domain. Adopting the lens of AI alignment, we categorize the prevailing methods and emergent proposals for the alignment of LLMs into outer and inner alignment. We also probe into salient issues including the models' interpretability, and potential vulnerabilities to adversarial attacks. To assess LLM alignment, we present a wide variety of benchmarks and evaluation methodologies. After discussing the state of alignment research for LLMs, we finally cast a vision toward the future, contemplating the promising avenues of research that lie ahead. Our aspiration for this survey extends beyond merely spurring research interests in this realm. We also envision bridging the gap between the AI alignment research community and the researchers engrossed in the capability exploration of LLMs for both capable and safe LLMs.Comment: 76 page

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

GEMv2 : Multilingual NLG benchmarking in a single line of code

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe

GEMv2 : Multilingual NLG benchmarking in a single line of code

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe

Insights into Paramyxovirus Nucleocapsids from Diverse Assemblies

All paramyxoviruses, which include the mumps virus, measles virus, Nipah virus, Newcastle disease virus, and Sendai virus, have non-segmented single-stranded negative-sense RNA genomes. These RNA genomes are enwrapped throughout the viral life cycle by nucleoproteins, forming helical nucleocapsids. In addition to these helical structures, recombinant paramyxovirus nucleocapsids may occur in other assembly forms such as rings, clam-shaped structures, and double-headed nucleocapsids; the latter two are composed of two single-stranded helices packed in a back-to-back pattern. In all of these assemblies, the neighboring nucleoprotein protomers adopt the same domain-swapping mode via the N-terminal arm, C-terminal arm, and recently disclosed N-hole. An intrinsically disordered region in the C-terminal domain of the nucleoproteins, called the N-tail, plays an unexpected role in regulating the transition among the different assembly forms that occurs with other viral proteins, especially phosphoprotein. These structures, together with the helical nucleocapsids, significantly enrich the structural diversity of the paramyxovirus nucleocapsids and help explain the functions of these diverse assemblies, including RNA genome protection, transcription, and replication, as well as encapsulation

Enhanced cytotoxicity of a redox-sensitive hyaluronic acid-based nanomedicine toward different oncocytes via various internalization mechanisms

Receptor-mediated active targeting and tumor microenvironment responsive systems from polymeric micelles have been studied for rapid cellular internalization and triggered drug release. Previously we have constructed redox-responsive polymeric micelles composed of vitamin E succinate conjugated hyaluronic acid (HA-ss-TOS), which are able to actively target CD44 proteins and quickly release loaded drugs upon exposure to high levels of glutathione (GSH) in tumor cells. In the present study, we found that despite different cellular internalization mechanisms, micelles showed strong antineoplastic effects on 4T1 and B16F10 cells due to redox responsiveness. HA-ss-TOS-PTX micelles exhibited an excellent tumor targeting ability and prolonged retention time compared to Taxol in vivo. In addition, a superior antitumor effect was achieved compared to PTX-loaded insensitive micelles (HA-TOS-PTX) and Taxol. Our results revealed that PTX-loaded HA-ss-TOS micelles could enhance the antineoplastic efficacy of PTX for breast cancer and melanoma treatment and, thus, deserve further attention

A Meta-Transcriptomics Survey Reveals Changes in the Microbiota of the Chinese Mitten Crab Eriocheir sinensis Infected with Hepatopancreatic Necrosis Disease

Infection of the freshwater Chinese mitten crab Eriocheir sinensis with hepatopancreatic necrosis disease (HPND) has been a major problem in the crab-cultivated Chinese Province of Jiangsu since 2015. To explore the etiology of HPND, meta-transcriptomic libraries of the hepatopancreata from crabs with and without HPND were constructed. Comparison analyses showed that there were no statistically significant differences in viral and microsporidial communities in the hepatopancreata of diseased and healthy crabs. Bacteroidetes, Proteobacteria, and Firmicutes were the most dominant bacterial phyla in the hepatopancreata of healthy crabs, with a combined prevalence of 93%. However, a decrease in bacterial diversity and a striking shift in the microbial composition were found in the hepatopancreata of crabs infected with HPND. Tenericutes was the most prevalent bacterial phylum in diseased crabs (31.82%), whereas its prevalence was low in healthy crabs (0.02%). By contrast, the prevalence of Bacteroidetes was significantly lower in crabs with HPND (3.49%) than in crabs without HPND (41.04%). We also found that the prevalence of Actinobacteria was higher in crabs with HPND (16.70%) than in crabs without the disease (4.03%). The major bacterial family within the Tenericutes phylum in crabs with HPND was detected by polymerase chain reaction and determined to be Mycoplasmataceae. In conclusion, there were striking changes in the microbiota of diseased and healthy crabs. Specifically, the prevalence of bacteria belonging to Tenericutes and Actinobacteria phyla increased, whereas the prevalence of bacteria belonging to the Bacteroidetes phylum decreased in crabs with HPND, clearly pointing to an association with HPND

Effects of UAV flight height on biomass estimation of desert shrub communities

Accurate estimation of desert vegetation biomass is crucial for monitoring changes in carbon stocks and productivity status. Unmanned aerial vehicle (UAV) remote sensing allows large-scale biomass surveys at the individual or patch scale. However, since desert shrubs are short and sparse, the UAV-based techniques do not always accurately capture biomass-related indicators at any flight height. This study investigated the effects of flight height on above-ground biomass (AGB) estimation using UAV images of typical shrub communities (Reaumuria soongarica) captured at different heights (i.e., 30 m, 50 m, 70 m, 90 m, 110 m, 130 m, and 150 m) in desert-grassland ecosystems. Several structural indicators associated with shrub allometric growth were extracted for AGB modeling, including canopy area (horizontal properties), canopy height (vertical properties), and canopy volume. Results revealed that the values of canopy height and volume decreased with increasing flight height, which made the poor performance of AGB models based on these indicators worse. For example, the variance explained (VE) of the models based on the mean canopy height decreased from about 62% to -137%, while the root mean square error (RMSE) increased from about 39 g to 92 g. In contrast, the canopy area was less affected by flight height, maintaining stable AGB models with VE around 72% and RMSE at 33 g. Adjusting the coefficients of linear models based on canopy height and volume with flight height significantly improved their predictive performance, with VE between 54% and 77% and RMSE between 30 g and 43 g for the optimized models based on mean canopy height. Furthermore, a higher flight height (e.g., 90–110 m) could be chosen to enhance operational efficiency while ensuring the accuracy of biomass observation. Our study offers valuable insights and guidance for vegetation surveys and research in desert-grassland ecosystems