JECS aggregates per-model conformal p-values via their maximum and reconstructs a conservative envelope of the max-p null distribution to select benchmarks with global contamination rate control.
GPT - N eo X -20 B : An open-source autoregressive language model
7 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
Unpack decomposes transformer credit via a unified backward recursion on the φ(S)U template, recovering known IOI circuits with mode labels and showing consistent duplicate-name suppression across Pythia scales from a single forward pass.
With enough compute, large models benefit from training on unfiltered data that includes low-quality and distractor examples instead of requiring high-quality filtered data.
At sufficient scale, LLMs linearly represent the truth value of factual statements, as shown by visualizations, cross-dataset generalization, and causal interventions that flip truth judgments.
BloombergGPT is a 50B parameter LLM trained on a 708B token mixed financial and general dataset that outperforms prior models on financial benchmarks while preserving general LLM performance.
SelfCheckGPT detects hallucinations by checking consistency across multiple sampled responses from black-box LLMs on WikiBio biography generation tasks.
Sessa integrates attention within recurrent paths to achieve power-law memory tails and flexible non-decaying selective retrieval, outperforming baselines on long-context tasks.
citing papers explorer
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Provable Joint Decontamination for Benchmarking Multiple Large Language Models
JECS aggregates per-model conformal p-values via their maximum and reconstructs a conservative envelope of the max-p null distribution to select benchmarks with global contamination rate control.
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Every Component is a Lookup: Token Attribution and Composition from a Single Decomposition
Unpack decomposes transformer credit via a unified backward recursion on the φ(S)U template, recovering known IOI circuits with mode labels and showing consistent duplicate-name suppression across Pythia scales from a single forward pass.
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A Bitter Lesson for Data Filtering
With enough compute, large models benefit from training on unfiltered data that includes low-quality and distractor examples instead of requiring high-quality filtered data.
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The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets
At sufficient scale, LLMs linearly represent the truth value of factual statements, as shown by visualizations, cross-dataset generalization, and causal interventions that flip truth judgments.
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BloombergGPT: A Large Language Model for Finance
BloombergGPT is a 50B parameter LLM trained on a 708B token mixed financial and general dataset that outperforms prior models on financial benchmarks while preserving general LLM performance.
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SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
SelfCheckGPT detects hallucinations by checking consistency across multiple sampled responses from black-box LLMs on WikiBio biography generation tasks.
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Sessa: Selective State Space Attention
Sessa integrates attention within recurrent paths to achieve power-law memory tails and flexible non-decaying selective retrieval, outperforming baselines on long-context tasks.