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Interleaves chain-of-thought reasoning with tool-using actions in one LLM loop: the model plans, queries a source like Wikipedia, then revises from results. Cuts hallucination versus reasoning-only prompting and beats trained agents on interactive tasks.
A graph-based RAG framework pairing a knowledge graph with vector retrieval and a dual-level (low/high) query mode. New documents merge into the graph via set operations instead of triggering a rebuild, cutting the cost of keeping the index current.
First model to make a decoder dynamically focus on different source words instead of cramming a whole sentence into one fixed vector — the soft-alignment idea that became "attention" and, three years later, powered the Transformer.
End-to-end encoder–decoder using deep LSTMs to map variable-length input sequences to output sequences; demonstrated competitive English→French translation (BLEU 34.8) and improved optimization by reversing source sentences, showing strong handling of long sentences.
The 2017 paper that replaced recurrence with pure self-attention, making sequence models fully parallelizable — and, almost as a side effect, laying the architectural foundation for nearly every large language model that followed, from BERT to GPT.
Embeds multi-head self-attention inside an LSTM-style memory, so stored memories can attend to one another instead of just sitting in separate slots — sharpening relational reasoning and topping WikiText-103, Project Gutenberg, and GigaWord.
Pre-trains a deep bidirectional Transformer encoder with masked-language-modeling and next-sentence prediction, then fine-tunes one model on 11 NLP tasks, reaching state-of-the-art on GLUE, SQuAD, and MultiNLI with little task-specific tuning.
Splits complex tasks into reusable subtasks, each with its own prompt or symbolic handler. Recursive decomposition and retrieval improve GPT-3 reasoning on symbolic tasks and multi-hop question answering.
Aligns language models by generating candidate outputs, filtering them with a reward model, and reusing the retained data for repeated fine-tuning. Translation experiments test this alternative to continual online sampling.
A concise textbook-style book that explains foundational concepts and techniques for large language models, covering pre-training, generative models, prompting, alignment, inference, and reasoning. Structured as self-contained chapters for readers with some ML/NLP background or those seeking a principled introduction to LLM foundations.
Compares standard human psychometric questionnaires (PVQ, BFI) with generation‑based profiling to test whether questionnaires predict real LLM responses. Finds big divergences: questionnaires exploit lexical cues and elicit alignment‑consistent answers, mischaracterizing LLM behavior on everyday queries.
An open large language model pairing DeepSeek Sparse Attention (DSA) for cheaper long-context inference with a scaled RL pipeline. Authors claim parity with GPT-5, with a high-compute Speciale variant surpassing it and rivaling Gemini-3.0-Pro on reasoning.