95 research outputs found
Towards Versatile and Efficient Visual Knowledge Integration into Pre-trained Language Models with Cross-Modal Adapters
Humans learn language via multi-modal knowledge. However, due to the
text-only pre-training scheme, most existing pre-trained language models (PLMs)
are hindered from the multi-modal information.
To inject visual knowledge into PLMs, existing methods incorporate either the
text or image encoder of vision-language models (VLMs) to encode the visual
information and update all the original parameters of PLMs for knowledge
fusion.
In this paper, we propose a new plug-and-play module, X-adapter, to flexibly
leverage the aligned visual and textual knowledge learned in pre-trained VLMs
and efficiently inject them into PLMs.
Specifically, we insert X-adapters into PLMs, and only the added parameters
are updated during adaptation.
To fully exploit the potential in VLMs, X-adapters consist of two
sub-modules, V-expert and T-expert, to fuse VLMs' image and text
representations, respectively.
We can opt for activating different sub-modules depending on the downstream
tasks.
Experimental results show that our method can significantly improve the
performance on object-color reasoning and natural language understanding (NLU)
tasks compared with PLM baselines
ChatEDA: A Large Language Model Powered Autonomous Agent for EDA
The integration of a complex set of Electronic Design Automation (EDA) tools
to enhance interoperability is a critical concern for circuit designers. Recent
advancements in large language models (LLMs) have showcased their exceptional
capabilities in natural language processing and comprehension, offering a novel
approach to interfacing with EDA tools. This research paper introduces ChatEDA,
an autonomous agent for EDA empowered by a large language model, AutoMage,
complemented by EDA tools serving as executors. ChatEDA streamlines the design
flow from the Register-Transfer Level (RTL) to the Graphic Data System Version
II (GDSII) by effectively managing task planning, script generation, and task
execution. Through comprehensive experimental evaluations, ChatEDA has
demonstrated its proficiency in handling diverse requirements, and our
fine-tuned AutoMage model has exhibited superior performance compared to GPT-4
and other similar LLMs
Large Language Models Cannot Self-Correct Reasoning Yet
Large Language Models (LLMs) have emerged as a groundbreaking technology with
their unparalleled text generation capabilities across various applications.
Nevertheless, concerns persist regarding the accuracy and appropriateness of
their generated content. A contemporary methodology, self-correction, has been
proposed as a remedy to these issues. Building upon this premise, this paper
critically examines the role and efficacy of self-correction within LLMs,
shedding light on its true potential and limitations. Central to our
investigation is the notion of intrinsic self-correction, whereby an LLM
attempts to correct its initial responses based solely on its inherent
capabilities, without the crutch of external feedback. In the context of
reasoning, our research indicates that LLMs struggle to self-correct their
responses without external feedback, and at times, their performance even
degrades after self-correction. Drawing from these insights, we offer
suggestions for future research and practical applications in this field.Comment: ICLR 202
A methane monitoring station siting method based on WRF-STILT and genetic algorithm
Reducing methane emissions in the oil and gas industry is a top priority for the current international community in addressing climate change. Methane emissions from the energy sector exhibit strong temporal variability and ground monitoring networks can provide time-continuous measurements of methane concentrations, enabling the rapid detection of sudden methane leaks in the oil and gas industry. Therefore, identifying specific locations within oil fields to establish a cost-effective and reliable methane monitoring ground network is an urgent and significant task. In response to this challenge, this study proposes a technical workflow that, utilizing emission inventories, atmospheric transport models, and intelligent computing techniques, automatically determines the optimal locations for monitoring stations based on the input quantity of monitoring sites. This methodology can automatically and quantitatively assess the observational effectiveness of the monitoring network. The effectiveness of the proposed technical workflow is demonstrated using the Shengli Oilfield, the second-largest oil and gas extraction base in China, as a case study. We found that the Genetic Algorithm can help find the optimum locations effectively. Besides, the overall observation effectiveness grew from 1.7 to 5.6 when the number of site increased from 1 to 9. However, the growth decreased with the increasing site number. Such a technology can assist the oil and gas industry in better monitoring methane emissions resulting from oil and gas extraction
Genome-Wide Association Study Reveals Candidate Genes for Growth Relevant Traits in Pigs
Improvement of the growth rate is a challenge in the pig industry, the Average Daily Gain (ADG) and Days (AGE) to 100 kg are directly related to growth performance. We performed genome-wide association study (GWAS) and genetic parameters estimation for ADG and AGE using the genomic and phonemic from four breed (Duroc, Yorkshire, Landrace, and Pietrain) populations. All analyses were performed by a multi-loci GWAS model, FarmCPU. The GWAS results of all four breeds indicate that five genome-wide significant SNPs were associated with ADG, and the nearby genomic regions explained 4.08% of the genetic variance and 1.90% of the phenotypic variance, respectively. For AGE, six genome-wide significant SNPs were detected, and the nearby genomic regions explained 8.09% of the genetic variance and 3.52% of phenotypic variance, respectively. In total, nine candidate genes were identified to be associated with growth and metabolism. Among them, TRIB3 was reported to associate with pig growth, GRP, TTR, CNR1, GLP1R, BRD2, HCRTR2, SEC11C, and ssc-mir-122 were reported to associate with growth traits in human and mouse. The newly detected candidate genes will advance the understanding of growth related traits and the identification of the novel variants will suggest a potential use in pig genomic breeding programs
Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be
utilized to add learnable parameters to Large Language Models (LLMs) without
increasing inference cost. Instruction tuning is a technique for training LLMs
to follow instructions. We advocate combining these two approaches, as we find
that MoE models benefit more from instruction tuning than dense models. In
particular, we conduct empirical studies across three experimental setups: (i)
Direct finetuning on individual downstream tasks devoid of instruction tuning;
(ii) Instructiontuning followed by in-context few-shot or zero-shot
generalization on downstream tasks; and (iii) Instruction tuning supplemented
by further finetuning on individual downstream tasks. In the first scenario,
MoE models overall underperform dense models of identical computational
capacity. This narrative, however, dramatically changes with the introduction
of instruction tuning (second and third scenario), used independently or in
conjunction with task-specific finetuning. Our most powerful model,
FLAN-MOE-32B, surpasses the performance of FLAN-PALM-62B on four benchmark
tasks, while using only a third of the FLOPs. The advancements embodied
byFLAN-MOE inspire a reevaluation of the design principles of large-scale,
high-performance language models in the framework of task-agnostic learning.Comment: Preprin
The Relations between Early-Life Stress and Risk, Time, and Prosocial Preferences in Adulthood: A Meta-Analytic Review
This meta-analytic review aims to address the mixed findings in previous research by quantifying the associations between early-life stress and risk, time, and prosocial preferences, and testing the boundary conditions of these associations. We meta-analyze 123 articles reporting 867 effect sizes among 199,019 adults to test different predictions from a life history perspective, a sensitization perspective, and an uncertainty management perspective about how early-life stress is associated with risk, time, and prosocial preferences
The relations between early-life stress and risk, time, and prosocial preferences in adulthood: A meta-analytic review
This meta-analytic review aims to address the mixed findings in previous research by quantifying the associations between early-life stress and risk, time, and prosocial preferences, and testing the boundary conditions of these associations. We meta-analyze 123 articles reporting 867 effect sizes among 199,019 adults to test different predictions from a life history perspective, a sensitization perspective, and an uncertainty management perspective about how early-life stress is associated with risk, time, and prosocial preferences. First, we find relatively small effect sizes indicating that early-life stress is associated with greater risk taking (r = .123), more present orientation (r = .126), and less prosociality (r = -.085), and its positive association with present orientation is stronger in currently stressful situations. Second, these observed associations do not vary significantly for harshness and unpredictability dimensions of early-life stress. Notably, moderation analyses across different types of preference measures only reveal an overall pattern of associations of early-life stress with self report measures of risk, time, and prosocial preferences. By contrast, early-life stress is not significantly associated with risk preference or prosocial preference measured with hypothetical choice tasks or laboratory behavior tasks. Taken together, although the overall pattern of results supports a life history perspective, a cautious interpretation is warranted by the variation in the results across different preference measures and potential publication bias in the results. More pre-registered studies are needed to test the extent to which preferences measured with arbitrary laboratory-based tasks capture real-world behaviors and to increase the ecological validity of laboratory-based measures.</p
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