In a new paper Shepherd: A Critic for Language Model Generation, a Meta AI research team presents Shepherd, a language model that are explicitly tuned to critique model generated outputs as well as to generate feedbacks to suggest improvements on solving the factuality, logical errors, coherence, and alignment issues.
In a new paper Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision, a Microsoft team research team presents Pareto optimal self-supervision, a flexible framework that leverages programmatic supervision to automatically calibrate and correct error for Large language models without extra manual efforts.
In a new paper FinGPT: Open-Source Financial Large Language Models, a research team from Columbia University and New York University (Shanghai) presents FinGPT, an end-to-end open-source financial large language models (FinLLMs) that democratize financial data to encourage researchers and practitioners to developer user-specified FinLLMs.
In the new paper Towards Healthy AI: Large Language Models Need Therapists Too, a team from Columbia University and IBM Research proposes SafeguardGPT, a framework that incorporates psychotherapy and reinforcement learning to correct the potentially harmful behaviours of AI chatbots.
In the new paper BloombergGPT: A Large Language Model for Finance, a research team from Bloomberg and Johns Hopkins University presents BloombergGPT, a 50 billion parameter language model trained on a 700 billion token dataset that significantly outperforms current benchmark models on financial tasks.
In the new paper GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models, a research team from OpenAI, OpenResearch, and the University of Pennsylvania investigates the potential impact of LLMs like GPT on the US labour market, shedding light on the economic, social, and policy implications.
In the new paper ViperGPT: Visual Inference via Python Execution for Reasoning, a Columbia University research team presents ViperGPT, a framework for solving complex visual queries by integrating code-generation models into vision via a Python interpreter. The proposed approach requires no further training and achieves state-of-the-art results.
In the new paper SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks, a research team from the University of California and Kuaishou Technology presents SpikeGPT, the first generative spiking neural network language model. The team’s largest, 260M parameter version achieves DNN-level performance while maintaining the energy efficiency of spike-based computations.
In the new paper Locating and Editing Factual Associations in GPT, a research team from MIT CSAIL, Northeastern University and Technion IIT examines how information flows during knowledge recall in large autoregressive transformers and introduces Rank-One Model Editing (ROME), a simple, zero-shot principled model editor capable of locating and editing factual associations in such models.
An OpenAI research team leverages reinforcement learning from human feedback (RLHF) to make significant progress on aligning language models with the users’ intentions. The proposed InstructGPT models are better at following instructions than GPT-3 while also more truthful and less toxic.