OPT releases open decoder-only transformers up to 175B parameters that match GPT-3 performance at one-seventh the carbon cost, along with code and training logs.
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LLMs recover dominant binomial orders from corpora but align less closely with exact preference distributions, with preference strength partially encoded in middle-to-late layers and manipulable via steering.
Gyan is a novel explainable non-transformer language model that achieves SOTA results on multiple datasets by mimicking human-like compositional context and world models.
citing papers explorer
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OPT: Open Pre-trained Transformer Language Models
OPT releases open decoder-only transformers up to 175B parameters that match GPT-3 performance at one-seventh the carbon cost, along with code and training logs.
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Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models
LLMs recover dominant binomial orders from corpora but align less closely with exact preference distributions, with preference strength partially encoded in middle-to-late layers and manipulable via steering.
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Gyan: An Explainable Neuro-Symbolic Language Model
Gyan is a novel explainable non-transformer language model that achieves SOTA results on multiple datasets by mimicking human-like compositional context and world models.