Search papers, labs, and topics across Lattice.
27 papers published across 0 labs.
Tabular foundation model performance hinges on the evaluation metric, revealing that no single pretraining objective is universally optimal across different risk profiles.
Bilingual language models can achieve performance comparable to monolingual models in both languages, challenging the assumption that bilingual input poses significant learning obstacles.
Forget expensive finetuning: DUME dynamically combines existing expert LLMs into a powerful MoE *without* additional training, unlocking multi-domain performance at minimal cost.
Unlock knowledge equity for underserved languages: L-ReLF offers a reproducible recipe for creating high-quality lexical datasets where they're needed most.
Despite its simple grammar, Esperanto translation still poses challenges for LLMs, with NLLB models only preferred in about half of human evaluations.
Tabular foundation model performance hinges on the evaluation metric, revealing that no single pretraining objective is universally optimal across different risk profiles.
Bilingual language models can achieve performance comparable to monolingual models in both languages, challenging the assumption that bilingual input poses significant learning obstacles.
Forget expensive finetuning: DUME dynamically combines existing expert LLMs into a powerful MoE *without* additional training, unlocking multi-domain performance at minimal cost.
Unlock knowledge equity for underserved languages: L-ReLF offers a reproducible recipe for creating high-quality lexical datasets where they're needed most.
Despite its simple grammar, Esperanto translation still poses challenges for LLMs, with NLLB models only preferred in about half of human evaluations.
Proprietary language models trounce open-source alternatives by 3-6x on a new, large-scale corpus of Sinhala and Pali Buddhist texts.
LLMs can mimic legislative reasoning, but their performance hinges on the proposal's idiosyncrasy, revealing a susceptibility to plausible-sounding confabulation that could mislead policymakers.
Real-time, uncertainty-aware signed distance functions are now possible without sacrificing accuracy, thanks to a novel kernel regression and GP regression hybrid.
Unlock new insights into rapid software development and collaboration with a massive dataset of over 100,000 hackathon projects.
Open-source projects are quietly integrating ML models in ways that may violate terms of service and regulations, raising concerns about unchecked ML automation.
VLMs can appear to gain up to 58% F1 on clinical tasks simply by *mentioning* MRI data in the prompt, even when the data is uninformative, revealing a "scaffold effect" that inflates performance metrics.
Random weight initialization is a major source of instability in deep learning, especially for rare classes, but this work shows how to eliminate it entirely with structured orthogonal initialization.
Forget pruning or quantization: MPO decomposition lets you compress a transformer by 13x while retaining 97% accuracy.
LLMs can now reliably transform messy app store reviews into well-formatted user stories, but still fall short of creating truly independent and unique requirements for agile development.
Quantum-proofing your 5G core doesn't have to break the bank: a sidecar proxy can add post-quantum cryptography with a predictable 50ms latency hit.
A task-specific, lightweight transformer can outperform state-of-the-art reasoning LLMs and commercial tools in C code vulnerability detection, at a fraction of the inference cost.
Forget fine-tuning: merging language-specific weights into instruction-tuned LLMs unlocks surprisingly effective instruction following in low-resource languages.
Blockchain-based federated learning can be made practical by using multi-task peer prediction to overcome the computational bottleneck of contribution measurement.
Synergy's architecture lets agents evolve through experience by proactively recalling rewarded trajectories, hinting at a new way to build agents that learn and adapt in open, collaborative environments.
Securing LLM supply chains requires cryptographically binding training and release claims to artifacts, enabling verifiable enforcement of security policies across teams and stages.
Bitcoin can be more than just digital gold: BitSov proposes a composable architecture for a censorship-resistant internet, anchored to Bitcoin's blockchain, that could reshape how we build decentralized applications.
Ditch the command line: these open-source Shiny apps make introductory statistics concepts like hypothesis testing and regression intuitively accessible to students without any programming experience.
Open-source RISC-V microcontrollers are now easier to build, thanks to a streamlined design and fully open RTL-to-GDS flow.
LLMs exhibit polarity illusions without rational inference, suggesting that "good enough" processing and partial grammaticalization may suffice to explain these phenomena in both machines and humans.
Adapting LLMs to low-resource languages might be as simple as teaching them to "speak" bytes, sidestepping the tokenization bottleneck.
Despite increased discussion around open science, replication studies in computing education research have only seen marginal growth, suggesting a disconnect between espoused values and actual research practices.
AI coding agents are less likely to break your code *except* when they're confidently "maintaining" it, where they're actually twice as risky as humans.
