Meta-Harness discovers improved harness code for LLMs via agentic search over prior execution traces, yielding 7.7-point gains on text classification with 4x fewer tokens and 4.7-point gains on math reasoning across held-out models.
Efficient intent detection with dual sentence encoders
9 Pith papers cite this work. Polarity classification is still indexing.
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SDBN introduces adversarial training to PEFT via two variants using character-level edits and LLM-generated perturbations, claiming improved robustness and generalization on NLP benchmarks in low-resource noisy settings.
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
Lightweight numerical bandits on text embeddings match or exceed LLM accuracy in contextual bandits at a fraction of the cost, with an embedding-based diagnostic to choose between them.
Capacity-aware dropping techniques mitigate load imbalance in MoE inference, delivering up to 1.85x speedup with 0.2% or less performance change on models including Mixtral-8x7B.
Introduces Adaptive Clustering router for MoE models that scales features to identify tight expert clusters, yielding faster convergence, robustness to corruption, and performance gains.
NV-Embed achieves first place on the MTEB leaderboard across 56 tasks by combining a latent attention layer, causal-mask removal, two-stage contrastive training, and data curation for LLM-based embedding models.
Data-CUBE applies a two-level curriculum (TSP-based task ordering via simulated annealing plus difficulty-sorted mini-batches) to multi-task instruction tuning and reports gains on MTEB sentence representation tasks.
LLMs fail to detect hidden harmful intent, allowing systematic bypass of safety mechanisms through framing techniques, with reasoning modes often worsening the issue.
citing papers explorer
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Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts
Capacity-aware dropping techniques mitigate load imbalance in MoE inference, delivering up to 1.85x speedup with 0.2% or less performance change on models including Mixtral-8x7B.
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Tight Clusters Make Specialized Experts
Introduces Adaptive Clustering router for MoE models that scales features to identify tight expert clusters, yielding faster convergence, robustness to corruption, and performance gains.