HyperPersona is a hypergraph framework that jointly models document, sentence, and word levels of text via hyperedges and nodes, then uses a transformer graph encoder to predict Big Five personality traits from text alone.
Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
5 Pith papers cite this work. Polarity classification is still indexing.
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Nomic AI produced and open-sourced a reproducible 8192-context English text embedder that exceeds OpenAI Ada-002 and text-embedding-3-small performance on MTEB short-context and LoCo long-context benchmarks.
TableMaster improves LM table understanding by verbalizing tables with enriched semantics and using adaptive textual-symbolic reasoning, reaching 78.13% accuracy on WikiTQ with GPT-4o-mini.
A survey deriving a unified policy gradient framework for LLM post-training methods and providing technical comparisons of PPO, GRPO, DPO variants.
citing papers explorer
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HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction
HyperPersona is a hypergraph framework that jointly models document, sentence, and word levels of text via hyperedges and nodes, then uses a transformer graph encoder to predict Big Five personality traits from text alone.
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Nomic Embed: Training a Reproducible Long Context Text Embedder
Nomic AI produced and open-sourced a reproducible 8192-context English text embedder that exceeds OpenAI Ada-002 and text-embedding-3-small performance on MTEB short-context and LoCo long-context benchmarks.
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TableMaster: A Recipe to Advance Table Understanding with Language Models
TableMaster improves LM table understanding by verbalizing tables with enriched semantics and using adaptive textual-symbolic reasoning, reaching 78.13% accuracy on WikiTQ with GPT-4o-mini.
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Reinforcement Learning for LLM Post-Training: A Survey
A survey deriving a unified policy gradient framework for LLM post-training methods and providing technical comparisons of PPO, GRPO, DPO variants.
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