This repository contains a regularly updated paper list for Large Language Models (LLMs) reasoning in latent space. Reasoning in latent space allows for more flexible and efficient thought representation beyond language tokens, bringing AI closer to human-like cognition. The repository covers various aspects of LLMs, including pre-training, supervised finetuning, analysis, interpretability, multimodal reasoning, and applications. It aims to showcase the advancements in reasoning with latent thoughts and continuous concepts in AI models.

Awesome-Story-Generation is a repository that curates a comprehensive list of papers related to Story Generation and Storytelling, focusing on the era of Large Language Models (LLMs). The repository includes papers on various topics such as Literature Review, Large Language Model, Plot Development, Better Storytelling, Story Character, Writing Style, Story Planning, Controllable Story, Reasonable Story, and Benchmark. It aims to provide a chronological collection of influential papers in the field, with a focus on citation counts for LLMs-era papers and some earlier influential papers. The repository also encourages contributions and feedback from the community to improve the collection.

A curated list of open-ended learning AI resources focusing on algorithms that invent new and complex tasks endlessly, inspired by human advancements. The repository includes papers, safety considerations, surveys, perspectives, and blog posts related to open-ended AI research.

The 'ai4math-papers' repository contains a collection of research papers related to AI applications in mathematics, including automated theorem proving, synthetic theorem generation, autoformalization, proof refactoring, premise selection, benchmarks, human-in-the-loop interactions, and constructing examples/counterexamples. The papers cover various topics such as neural theorem proving, reinforcement learning for theorem proving, generative language modeling, formal mathematics statement curriculum learning, and more. The repository serves as a valuable resource for researchers and practitioners interested in the intersection of AI and mathematics.

The repository 'Awesome LLM Reasoning Openai-o1 Survey' provides a collection of survey papers and related works on OpenAI o1, focusing on topics such as LLM reasoning, self-play reinforcement learning, complex logic reasoning, and scaling law. It includes papers from various institutions and researchers, showcasing advancements in reasoning bootstrapping, reasoning scaling law, self-play learning, step-wise and process-based optimization, and applications beyond math. The repository serves as a valuable resource for researchers interested in exploring the intersection of language models and reasoning techniques.

LLM-IR-Bias-Fairness-Survey is a collection of papers related to bias and fairness in Information Retrieval (IR) with Large Language Models (LLMs). The repository organizes papers according to a survey paper titled 'Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era'. The survey provides a comprehensive review of emerging issues related to bias and unfairness in the integration of LLMs into IR systems, categorizing mitigation strategies into data sampling and distribution reconstruction approaches.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

This repository contains a curated list of resources on generative information retrieval, including research papers, datasets, tools, and applications. Generative information retrieval is a subfield of information retrieval that uses generative models to generate new documents or passages of text that are relevant to a given query. This can be useful for a variety of tasks, such as question answering, summarization, and document generation. The resources in this repository are intended to help researchers and practitioners stay up-to-date on the latest advances in generative information retrieval.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

Awesome deep logic is a curated list of papers and resources focusing on integrating symbolic logic into deep neural networks. It includes surveys, tutorials, and research papers that explore the intersection of logic and deep learning. The repository aims to provide valuable insights and knowledge on how logic can be used to enhance reasoning, knowledge regularization, weak supervision, and explainability in neural networks.

This repository, Awesome-LLM-RAG, aims to record advanced papers on Retrieval Augmented Generation (RAG) in Large Language Models (LLMs). It serves as a resource hub for researchers interested in promoting their work related to LLM RAG by updating paper information through pull requests. The repository covers various topics such as workshops, tutorials, papers, surveys, benchmarks, retrieval-enhanced LLMs, RAG instruction tuning, RAG in-context learning, RAG embeddings, RAG simulators, RAG search, RAG long-text and memory, RAG evaluation, RAG optimization, and RAG applications.

LLM4DB is a repository focused on the intersection of Large Language Models (LLM) and Database technologies. It covers various aspects such as data processing, data analysis, database optimization, and data management for LLM. The repository includes works on data cleaning, entity matching, schema matching, data discovery, NL2SQL, data exploration, data visualization, configuration tuning, query optimization, and anomaly diagnosis using LLMs. It aims to provide insights and advancements in leveraging LLMs for improving data processing, analysis, and database management tasks.

LLM4DB is a repository focused on the intersection of Large Language Models (LLMs) and Database technologies. It covers various aspects such as data processing, data analysis, database optimization, and data management for LLMs. The repository includes research papers, tools, and techniques related to leveraging LLMs for tasks like data cleaning, entity matching, schema matching, data discovery, NL2SQL, data exploration, data visualization, knob tuning, query optimization, and database diagnosis.

This repository contains a regularly updated paper list for Large Language Models (LLMs) reasoning in latent space. Reasoning in latent space allows for more flexible and efficient thought representation beyond language tokens, bringing AI closer to human-like cognition. The repository covers various aspects of LLMs, including pre-training, supervised finetuning, analysis, interpretability, multimodal reasoning, and applications. It aims to showcase the advancements in reasoning with latent thoughts and continuous concepts in AI models.

The GraphRAG project is a data pipeline and transformation suite designed to extract meaningful, structured data from unstructured text using LLMs. It enhances LLMs' ability to reason about private data. The repository provides guidance on using knowledge graph memory structures to enhance LLM outputs, with a warning about the potential costs of GraphRAG indexing. It offers contribution guidelines, development resources, and encourages prompt tuning for optimal results. The Responsible AI FAQ addresses GraphRAG's capabilities, intended uses, evaluation metrics, limitations, and operational factors for effective and responsible use.

This repository contains a curated list of papers focusing on the self-correction of large language models (LLMs) during inference. It covers various frameworks for self-correction, including intrinsic self-correction, self-correction with external tools, self-correction with information retrieval, and self-correction with training designed specifically for self-correction. The list includes survey papers, negative results, and frameworks utilizing reinforcement learning and OpenAI o1-like approaches. Contributions are welcome through pull requests following a specific format.

ERNIE 4.5 is a family of large-scale multimodal models with 10 distinct variants, including Mixture-of-Experts (MoE) models with 47B and 3B active parameters. The models feature a novel heterogeneous modality structure supporting parameter sharing across modalities while allowing dedicated parameters for each individual modality. Trained with optimal efficiency using PaddlePaddle deep learning framework, ERNIE 4.5 models achieve state-of-the-art performance across text and multimodal benchmarks, enhancing multimodal understanding without compromising performance on text-related tasks. The open-source development toolkits for ERNIE 4.5 offer industrial-grade capabilities, resource-efficient training and inference workflows, and multi-hardware compatibility.

LLM-KG4QA is a repository focused on the integration of Large Language Models (LLMs) and Knowledge Graphs (KGs) for Question Answering (QA). It covers various aspects such as using KGs as background knowledge, reasoning guideline, and refiner/filter. The repository provides detailed information on pre-training, fine-tuning, and Retrieval Augmented Generation (RAG) techniques for enhancing QA performance. It also explores complex QA tasks like Explainable QA, Multi-Modal QA, Multi-Document QA, Multi-Hop QA, Multi-run and Conversational QA, Temporal QA, Multi-domain and Multilingual QA, along with advanced topics like Optimization and Data Management. Additionally, it includes benchmark datasets, industrial and scientific applications, demos, and related surveys in the field.

This prototype synthesizes verified code with an LLM using Monte Carlo Tree Search (MCTS). It explores the space of possible generation of a verified program and checks at every step that it's on the right track by calling the verifier. This prototype uses Dafny, Coq, Lean, Scala, or Rust. By using this technique, weaker models that might not even know the generated language all that well can compete with stronger models.

FlashInfer is a library for Language Languages Models that provides high-performance implementation of LLM GPU kernels such as FlashAttention, PageAttention and LoRA. FlashInfer focus on LLM serving and inference, and delivers state-the-art performance across diverse scenarios.

Dolma is a dataset and toolkit for curating large datasets for (pre)-training ML models. The dataset consists of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials. The toolkit provides high-performance, portable, and extensible tools for processing, tagging, and deduplicating documents. Key features of the toolkit include built-in taggers, fast deduplication, and cloud support.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.