Gemma-2-2B
PulseAugur coverage of Gemma-2-2B — every cluster mentioning Gemma-2-2B across labs, papers, and developer communities, ranked by signal.
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CorrSteer method enhances LLM steering using correlated sparse autoencoder features
Researchers have developed CorrSteer, a novel method for steering large language models (LLMs) during generation using features extracted from Sparse Autoencoders (SAEs). This technique correlates sample correctness wit…
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DB-KSVD algorithm offers scalable approach to disentangling high-dimensional embedding spaces
Researchers have introduced DB-KSVD, a novel dictionary learning algorithm designed to disentangle high-dimensional embedding spaces in large transformer models. This method adapts the classic KSVD algorithm to scale ef…
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LLM jailbreaks linked to mid-to-late layer feature vulnerabilities
Researchers have developed a method to identify specific internal features within large language models that contribute to their vulnerability to jailbreaking attacks. By analyzing the Gemma-2-2B model using the BeaverT…
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Google unveils Simula and CTCL for advanced synthetic data generation
Google Research has introduced Simula, a framework that treats synthetic data generation as a mechanism design problem. This approach allows for fine-grained control over dataset characteristics like coverage, complexit…
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Google releases Gemma 2 2B, ShieldGemma, and Gemma Scope
Google has announced updates to its Gemma family of models, including the release of Gemma 2 2B. This new iteration is designed for efficiency and accessibility, aiming to empower developers with powerful yet lightweigh…
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Google DeepMind releases T5Gemma encoder-decoder LLMs adapted from Gemma
Google DeepMind has introduced T5Gemma, a new family of encoder-decoder large language models derived from their existing Gemma 2 models. This adaptation technique allows for flexible combinations of encoder and decoder…