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6,313 papers
Language-Guided Tuning is introduced, a framework that employs multi-agent Large Language Models to automatically optimize configurations through natural language reasoning to demonstrate substantial improvements over traditional optimization methods while maintaining high interpretability.
RNop represents a shift in mRNA optimization methodology: by infusing explicit and interpretable knowledge, the"black-box"mRNA design can be transformed into a predictable, explainable engineering problem.
This work develops a new SpFT framework, based on ideas from neural network pruning, that improves SpFT's memory efficiency by 20-50\% while matching the accuracy of state-of-the-art methods like LoRA's variants.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
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PQMass provides a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models and scales well to moderately high-dimensional data and thus obviates the need for feature extraction in practical applications.
It is discovered that the energy benefits of quantum computing economies are contingent on large-scale computation, and quantum computing may represent a more sustainable pathway for the computing industry.
CON decomposition is introduced, which quantifies how much of a layer's variance each concept explains given all other concepts and the outcome, and how much none of them explains, yielding layer-comparable, calibrated scores that suppress false positives.
This paper argues that the current main approaches in multi-objective RL (SER and ESR), and successor features, are insufficient, and motivates that this can indeed be the case by an example, leading to a new perspective, and a significant and non-trivial gap in the literature.
We consider the problem of learning a mixture of $k$ Plackett-Luce models given multi-way ranking responses from annotators that may represent heterogeneous underlying preferences. This problem has many applications in AI alignment and preference optimization. Prior work has studied mixtures of Bradley-Terry models from pairwise comparisons. However, uncovering mixture models is theoretically unidentifiable when $k$ exceeds $m/2$, where $m$ is the length of a ranking. We propose an efficient implementation to address this limitation, which involves first augmenting the rankings to a larger size by generating new responses from a base language model, followed by a gradient-based estimation to reduce inference cost in the input embedding space. Based on this procedure, we then design an expectation-maximization algorithm with these two steps to fit a mixture of Plackett-Luce models, called MoPLEx. Extensive experiments are conducted to verify this approach. First, we show that the gradient-based approximation estimates true probabilities with less than 5% error on models with up to 34 billion parameters. Second, we show that MoPLEx improves clustering and ranking accuracy by an average of 43.7% and 15.2% over baselines using single ranking and mixtures of Bradley-Terry models, on preference optimization datasets. These results demonstrate the effectiveness of MoPLEx for tackling multi-way rankings from heterogeneous preferences through measuring alignment between gradients.
DeMMO is proposed, an interpretable framework for longitudinal, multi-disease, and multi-outcome learning that infers signed relations directly from learned longitudinal DMO-outcome mappings, thereby enabling selective information sharing across cohorts without requiring paired participants.
A novel Fisher information-based Adaptive Mixed Precision Weight Quantization approach, i.e., FAMPWQ, which performs layer-adaptive weight quantization for effective LLM inference on commodity GPUs and a reinforcement learning-based bit-width allocator in FAMPWQ, which generates an adaptive bit-width allocation strategy based on the Fisher information sensitivity metric.
Across various benchmarks, it is shown that the representations learned by Mol-JEPA deliver strong performance, demonstrating the value of incorporating biochemical context through latent space prediction.
Machine Intelligence
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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