Develops a model-agnostic attribution score as the log-ratio of conditional response probabilities with and without a marginalized prompt token, derived via Bayes inversion of next-token distributions, and relates it to conditional entropies.
A Primer in BERT ology: What We Know About How BERT Works
8 Pith papers cite this work. Polarity classification is still indexing.
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RISE is an inference-time semantic reranking framework that refines low-confidence predictions in rhetorical role labeling using contrastively learned label representations, delivering an average +9.15 macro-F1 gain on hard examples across eight datasets and seven models.
LLMs achieve strong results on syntax parsing tasks but show limited and variable performance on dynamic reasoning, with a clear performance hierarchy across model scales.
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.
Training a mean-field Transformer under L2 regularization induces an escape from attention-driven token clustering in later layers after initial clustering.
Search-R3 trains LLMs to output search embeddings as a direct product of step-by-step reasoning via supervised pre-training and a specialized RL environment that avoids full corpus re-encoding.
Inflectional features stay linearly decodable across all layers while lexical identity weakens with depth in modern transformers.
Probing classifiers are a common but limited method for analyzing linguistic knowledge in neural NLP models, and this review outlines their promises, methodological shortcomings, and recent advances.
citing papers explorer
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Probabilistic Attribution For Large Language Models
Develops a model-agnostic attribution score as the log-ratio of conditional response probabilities with and without a marginalized prompt token, derived via Bayes inversion of next-token distributions, and relates it to conditional entropies.
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Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling
RISE is an inference-time semantic reranking framework that refines low-confidence predictions in rhetorical role labeling using contrastively learned label representations, delivering an average +9.15 macro-F1 gain on hard examples across eight datasets and seven models.
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Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs
LLMs achieve strong results on syntax parsing tasks but show limited and variable performance on dynamic reasoning, with a clear performance hierarchy across model scales.
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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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Training-Induced Escape from Token Clustering in a Mean-Field Formulation of Transformers
Training a mean-field Transformer under L2 regularization induces an escape from attention-driven token clustering in later layers after initial clustering.
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Search-R3: Unifying Reasoning and Embedding in Large Language Models
Search-R3 trains LLMs to output search embeddings as a direct product of step-by-step reasoning via supervised pre-training and a specialized RL environment that avoids full corpus re-encoding.
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Model Internal Sleuthing: Finding Lexical Identity and Inflectional Features in Modern Language Models
Inflectional features stay linearly decodable across all layers while lexical identity weakens with depth in modern transformers.
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Probing Classifiers: Promises, Shortcomings, and Advances
Probing classifiers are a common but limited method for analyzing linguistic knowledge in neural NLP models, and this review outlines their promises, methodological shortcomings, and recent advances.