HeRo recursively partitions the LLM vocabulary into a hierarchy, embedding multi-bit payloads across layers so that verifiers with different keys recover only their authorized portion while preserving the original sampling distribution.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
Debiasing language-model training data for a target group frequently increases stereotyping or counter-stereotyping for non-target groups across categories, models, and scales.
A two-stream Transformer variant that separates state storage from next-token prediction improves validation loss and downstream task performance by 2-3 points over standard Transformers.
Introduces predictive prefetching for RAG that anticipates retrieval needs several tokens ahead via three components, reporting up to 43.5% latency reduction and 62.4% TTFT improvement while preserving answer quality.
GTLM injects graph-aware attention biases into LLMs using only 0.015% extra parameters, enabling native graph processing that matches 7B models with a 1B model on text-attributed graph benchmarks.
Truncated embeddings from non-MRL models perform comparably to or better than MRL-trained models for most truncation levels, except heavy truncation of 80% or more.
citing papers explorer
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Selective Disclosure Watermarking for Large Language Models
HeRo recursively partitions the LLM vocabulary into a hierarchy, embedding multi-bit payloads across layers so that verifiers with different keys recover only their authorized portion while preserving the original sampling distribution.
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When Debiasing Backfires: Counterintuitive Side Effects of Preprocessing-Based Stereotype Mitigation
Debiasing language-model training data for a target group frequently increases stereotyping or counter-stereotyping for non-target groups across categories, models, and scales.
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The State-Prediction Separation Hypothesis
A two-stream Transformer variant that separates state storage from next-token prediction improves validation loss and downstream task performance by 2-3 points over standard Transformers.
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Predictive Prefetching for Retrieval-Augmented Generation
Introduces predictive prefetching for RAG that anticipates retrieval needs several tokens ahead via three components, reporting up to 43.5% latency reduction and 62.4% TTFT improvement while preserving answer quality.
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Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
GTLM injects graph-aware attention biases into LLMs using only 0.015% extra parameters, enabling native graph processing that matches 7B models with a 1B model on text-attributed graph benchmarks.
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To MRL or not to MRL: Text Embeddings are Robust to Truncation Without Matryoshka Learning, Except In Heavy Truncation Scenarios
Truncated embeddings from non-MRL models perform comparably to or better than MRL-trained models for most truncation levels, except heavy truncation of 80% or more.