awesome-InteractiveNLP-papers
Paper List for a new paradigm of NLP: Interactive NLP (https://arxiv.org/abs/2305.13246) :fire:
https://github.com/InteractiveNLP-Team/awesome-InteractiveNLP-papers
Last synced: 3 days ago
JSON representation
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Paper
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📚KB-LM Interaction
- K-BERT: Enabling Language Representation with Knowledge Graph - K--BERT-blue)  
- ERNIE: Enhanced Representation through Knowledge Integration - ERNIE-blue) 
- Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments - Pangu-blue)  
- Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback - LLM--Augmenter-blue)  
- WebGPT: Browser-assisted question-answering with human feedback - WebGPT-blue)  
- Atlas: Few-shot learning with retrieval augmented language models - Atlas-blue) 
- WebGPT: Browser-assisted question-answering with human feedback - WebGPT-blue)  
- Improving language models by retrieving from trillions of tokens - Retro-blue)  
- Binding Language Models in Symbolic Languages - Binder-blue)   
- Teaching language models to support answers with verified quotes - GopherCite-blue)  
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - RAG-blue) 
- MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge - MineDojo-blue)  
- REALM: retrieval-augmented language model pre-training - REALM-blue)  
- KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs - KELM-blue)  
- K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters - K--Adapter-blue)   
- Binding Language Models in Symbolic Languages - Binder-blue)   
- Teaching language models to support answers with verified quotes - GopherCite-blue)  
- DocPrompting: Generating Code by Retrieving the Docs - DocPrompting-blue)   
- Recitation-Augmented Language Models - RECITE-blue) 
- Mind's Eye: Grounded Language Model Reasoning through Simulation - Mind%E2%80%99s%20Eye-blue)  
- Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments - Pangu-blue)  
- Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback - LLM--Augmenter-blue)  
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👨💼Human-LM Interaction
- Deep reinforcement learning from human preferences - Reinforcement%20Learning-orange) 
- Constitutional Al:learning from ai feedback - Constitutional%20AI-blue)  
- RRHF: Rank Responses to Align Language Models with Human Feedback without tears - RRHF-blue)  
- RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment - RAFT-blue) 
- Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback - Natural%20Language-lightgrey) 
- Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization - RL4LMs-blue)  
- Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement - TeachMe-blue)   
- MemPrompt: Memory-assisted Prompt Editing with User Feedback - MemPrompt-blue)   
- Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code - green)
- LaMP: When Large Language Models Meet Personalization - LaMP-blue) 
- Languages are Rewards: Hindsight Finetuning using Human Feedback - Chain%20of%20Hindsight-blue)  
- InternChat: Solving Vision-Centric Tasks by Interacting with Chatbots Beyond Language - InternChat-blue)  
- Improving Code Generation by Training with Natural Language Feedback - ILF-blue)   
- PromptChainer: Chaining Large Language Model Prompts through Visual Programming - PromptChainer-blue)   
- Training language models to follow instructions with human feedback - InstructGPT-blue)  
- Improving alignment of dialogue agents via targeted human judgements - Sparrow-blue)  
- AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts - AI%20Chains-blue)   
- Interactive Text Generation - ITG-blue)   
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🤖Model/🛠Tool-LM Interaction
- Small Models are valuable Plug-ins for large language models - SuperICL-blue) 
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models - ToT-blue)  
- RECURRENTGPT: Interactive Generation of (Arbitrarily) Long Text - RecurrentGPT-blue) 
- Internet-augmented language models through few-shot prompting for open-domain question answering - -use-green)
- Internet-augmented language models through few-shot prompting for open-domain question answering - -use-green)
- Recitation-Augmented Language Models - RECITE-blue)   
- Iteratively Prompt Pre-trained Language Models for Chain of Thought - Prompt%20Chaining-orange)  
- ViperGPT: Visual Inference via Python Execution for Reasoning - ViperGPT-blue)   
- OpenAGI: When LLM Meets Domain Experts - OpenAGI-blue)   
- ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions - ChatCaptioner-blue)   
- Generative Agents: Interactive Simulacra of Human Behavior - Generative%20Agents-blue)  
- ReAct: Synergizing reasoning and acting in language models - ReAct-blue)  
- Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models - Visual%20ChatGPT-blue) 
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face - HuggingGPT-blue) 
- CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society - CAMEL-blue)  
- Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks - PoT%20Prompting-blue)   
- LEAST-TO-MOST PROMPTINGENABLESCOMPLEXREASONING IN LARGE LANGUAGE MODELS - least--to--most%20prompting-blue)  
- ViperGPT: Visual Inference via Python Execution for Reasoning - ViperGPT-blue)   
- See,Think,Confirm:Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning - IPVR-blue)  
- Large Language Models Are Reasoning Teachers - Fine--tune--CoT-blue)  
- STaR:Self-Taught ReasonerBootstrapping Reasoning With Reasoning - STaR-blue)  
- Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks - SearChain-blue)  
- PAL: Program-aided Language Models - PAL-blue)   
- Recitation-Augmented Language Models - RECITE-blue)   
- Iteratively Prompt Pre-trained Language Models for Chain of Thought - Prompt%20Chaining-orange)  
- MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS - self--ask-blue)  
- Self-Refine: Iterative Refinement with Self-Feedback - self--refine-blue)   
- LEVER: Learning to Verify Language-to-Code Generation with Execution - LEVER-blue)   
- Computational Language Acquisition with Theory of Mind - green)
- Few-shot Language Coordination by Modeling Theory of Mind - green)
- OpenAGI: When LLM Meets Domain Experts - OpenAGI-blue)   
- MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action - MM--ReAct-blue)  
- Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners - CaFo-blue)   
- Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision - SELF--ALIGN-blue)   
- Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions - Elicitive%20Prompting-orange)  
- TALM: Tool Augmented Language Models - TALM-blue)   
- Successive Prompting for Decomposing Complex Questions - Successive%20Prompting-blue)  
- REFINER: Reasoning Feedback on Intermediate Representations - REFINER-blue)   
- LeTI: Learning to Generate from Textual Interactions - LeTI-blue)   
- InternGPT: Solving Vision-Centric Tasks by Interacting with Chatbots Beyond Language - InternGPT-blue)  
- Human-level play in the game of Diplomacy by combining language models with strategic reasoning - Cicero-blue)   
- Generating Sequences by Learning to Self-Correct - Self--Correction-blue)   
- ChatGPT-steered Editing Instructor for Customization of Abstractive Summarization - Semi--Supervised%20Fine--Tuning,%20Prompt%20Chaining-orange)  
- ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions - ChatCaptioner-blue)   
- Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data - Baize-blue)   
- Small Models are valuable Plug-ins for large language models - SuperICL-blue) 
- See,Think,Confirm:Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning - IPVR-blue)  
- STaR:Self-Taught ReasonerBootstrapping Reasoning With Reasoning - STaR-blue)  
- MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS - self--ask-blue)  
- LEVER: Learning to Verify Language-to-Code Generation with Execution - LEVER-blue)   
- Computational Language Acquisition with Theory of Mind - green)
- Few-shot Language Coordination by Modeling Theory of Mind - green)
- Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners - CaFo-blue)   
- Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision - SELF--ALIGN-blue)   
- Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions - Elicitive%20Prompting-orange)  
- TALM: Tool Augmented Language Models - TALM-blue)   
- Successive Prompting for Decomposing Complex Questions - Successive%20Prompting-blue)  
- REFINER: Reasoning Feedback on Intermediate Representations - REFINER-blue)   
- InternGPT: Solving Vision-Centric Tasks by Interacting with Chatbots Beyond Language - InternGPT-blue)  
- ChatGPT-steered Editing Instructor for Customization of Abstractive Summarization - Semi--Supervised%20Fine--Tuning,%20Prompt%20Chaining-orange)  
- Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models - Chameleon-blue)  
- Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data - Baize-blue)   
- MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks - MindCraft-blue)   
- Toolformer: Language Models Can Teach Themselves to Use Tools - Toolformer-blue) 
- ART: Automatic multi-step reasoning and tool-use for large language models - ART-blue)  
- Decomposed Prompting: A Modular Approach for Solving Complex Tasks - Decomposed%20Prompting-blue)  
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models - ToT-blue)  
- Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks - SearChain-blue)  
- RECURRENTGPT: Interactive Generation of (Arbitrarily) Long Text - RecurrentGPT-blue) 
- PAL: Program-aided Language Models - PAL-blue)   
- Self-Refine: Iterative Refinement with Self-Feedback - self--refine-blue)   
- LeTI: Learning to Generate from Textual Interactions - LeTI-blue)   
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🌎Environment-LM Interaction
- PaLM-E: An Embodied Multimodal Language Model - PaLM--E-blue)
- Mind's Eye: Grounded Language Model Reasoning through Simulation - Elicitive%20Prompting-orange) 
- BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models - BLIP--2-blue)   
- RT-1: Robotics Transformer for Real-World Control at Scale - RT--1-blue)
- Open-vocabulary Queryable Scene Representations for Real World Planning - NLMap-blue)   
- Plan4MC: Skill Reinforcement Learning and Planning for Open-World Minecraft Tasks - Plan4MC-blue)   
- MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge - MineDojo-blue)  
- LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action - -Nav-blue)
- WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents - blue)  
- VIMA: General Robot Manipulation with Multimodal Prompts - VIMA-blue)  
- PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World - PIGLeT-blue)  
- Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents - Prompt%20Chaining,%20Reinforcement%20Learning-orange) 
- ProgPrompt: Generating Situated Robot Task Plans using Large Language Models - ProgPrompt-blue)   
- Collaborating with language models for embodied reasoning - -Actor--Reporter-blue) 
- Open-World Object Manipulation using Pre-trained Vision-Language Models - MOO-blue)
- Guiding Pretraining in Reinforcement Learning with Large Language Models - ELLM-blue)  
- Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning - GLAM-blue)  
- Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments - Prompt%20Chaining-orange)
- Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language - blue)  
- Interactive Language: Talking to Robots in Real Time - blue) 
- Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback - Reinforcement%20Learning-orange)
- Do As I Can, Not As I Say: Grounding Language in Robotic Affordances - SayCan-blue)  
- Grounded decoding: Guiding text generation with grounded models for robot control - blue)  
- Inner monologue: Embodied reasoning through planning with language models. - blue) 
- Code as Policies: Language Model Programs for Embodied Control - blue) 
- Plan,Eliminate,and Track-Language Models are Good Teachers for Embodied Agents. - Standard%20Prompting,%20Prompt%20Chaining-orange)  
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👍Evaluation
- Evaluating Human-Language Model Interaction - HALIE-blue) 
- Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation - Behavior--1k-blue) 
- An AI Dungeon Master’s Guide: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons - -of--Mind-green)
- Evaluating Verifiability in Generative Search Engines
- Alexa Arena: A User-Centric Interactive Platform for Embodied AI - Alexa%20Arena-blue) 
- ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness - ReCEval-blue)  
- Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models - Multi--Party%20Chat-blue) 
- ORBIT: A Unified Simulation Framework for Interactive Robot Learning Environments - ORBIT-blue) 
- Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering - ScienceQA-blue) 
- Alexa Arena: A User-Centric Interactive Platform for Embodied AI - Alexa%20Arena-blue) 
- CB2: Collaborative Natural Language Interaction Research Platform - CB2-blue)  
- Evaluating Human-Language Model Interaction - HALIE-blue) 
- ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness - ReCEval-blue)  
- Evaluating Verifiability in Generative Search Engines
- Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models - Multi--Party%20Chat-blue) 
- ORBIT: A Unified Simulation Framework for Interactive Robot Learning Environments - ORBIT-blue) 
- Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering - ScienceQA-blue) 
- CB2: Collaborative Natural Language Interaction Research Platform - CB2-blue)  
- An AI Dungeon Master’s Guide: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons - -of--Mind-green)
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🔭Surveys and Position Papers
- Interactive Natural Language Processing - green)
- Tool Learning with Foundation Models - Tool--use-green)
- Augmented Language Models: a Survey - Reasoning-green) 
- Foundation Models for Decision Making: Problems, Methods, and Opportunities - Tool--use-green) 
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Contributors
- ![img - Team/awesome-InteractiveNLP-papers/graphs/contributors)
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Contributing to this paper list
- ![Star History Chart - history.com/#InteractiveNLP-Team/awesome-InteractiveNLP-papers&Date)
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