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Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs

CategoriesAI-ML_, Issue 2023Q2, Site Updates_

From: https://www.mosaicml.com/blog/mpt-7b

Introducing MPT-7B, the latest entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Starting today, you can train, finetune, and deploy your own private MPT models, either starting from one of our checkpoints or training from scratch. For inspiration, we are also releasing three finetuned models in addition to the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-StoryWriter-65k+, the last of which uses a context length of 65k tokens!

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[D] AI Policy Group CAIDP Asks FTC To Stop OpenAI From Launching New GPT Models

CategoriesAI-ML_, Issue 2023Q2, Site Updates_

Via the agile u/ vadhavaniyafaijan :

The Center for AI and Digital Policy (CAIDP), a tech ethics group, has asked the Federal Trade Commission to investigate OpenAI for violating consumer protection rules. CAIDP claims that OpenAI’s AI text generation tools have been “biased, deceptive, and a risk to public safety.”

CAIDP’s complaint raises concerns about potential threats from OpenAI’s GPT-4 generative text model, which was announced in mid-March. It warns of the potential for GPT-4 to produce malicious code and highly tailored propaganda and the risk that biased training data could result in baked-in stereotypes or unfair race and gender preferences in hiring.

The complaint also mentions significant privacy failures with OpenAI’s product interface, such as a recent bug that exposed OpenAI ChatGPT histories and possibly payment details of ChatGPT plus subscribers.

CAIDP seeks to hold OpenAI accountable for violating Section 5 of the FTC Act, which prohibits unfair and deceptive trade practices. The complaint claims that OpenAI knowingly released GPT-4 to the public for commercial use despite the risks, including potential bias and harmful behavior.

Source | Case| PDF

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[R] Hello Dolly: Democratizing the magic of ChatGPT with open models

CategoriesAI-ML_, Issue 2023Q2, Site Updates_

Databricks shows that anyone can take a dated off-the-shelf open source large language model (LLM) and give it magical ChatGPT-like instruction following ability by training it in less than three hours on one machine, using high-quality training data.

They fine tuned GPT-J using the Alpaca dataset.

Blog: https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html
Github: https://github.com/databrickslabs/dolly

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[R] Reflexion: an autonomous agent with dynamic memory and self-reflection – Noah Shinn et al 2023 Northeastern University Boston – Outperforms GPT-4 on HumanEval accuracy (0.67 –> 0.88)!

CategoriesAI-ML_, Issue 2023Q2, Site Updates_

Paper: https://arxiv.org/abs/2303.11366

Blog: https://nanothoughts.substack.com/p/reflecting-on-reflexion

Github: https://github.com/noahshinn024/reflexion-human-eval

Twitter: https://twitter.com/johnjnay/status/1639362071807549446?s=20

Abstract:

Recent advancements in decision-making large language model (LLM) agents have demonstrated impressive performance across various benchmarks. However, these state-of-the-art approaches typically necessitate internal model fine-tuning, external model fine-tuning, or policy optimization over a defined state space. Implementing these methods can prove challenging due to the scarcity of high-quality training data or the lack of well-defined state space. Moreover, these agents do not possess certain qualities inherent to human decision-making processes, specifically the ability to learn from mistakes. Self-reflection allows humans to efficiently solve novel problems through a process of trial and error. Building on recent research, we propose Reflexion, an approach that endows an agent with dynamic memory and self-reflection capabilities to enhance its existing reasoning trace and task-specific action choice abilities. To achieve full automation, we introduce a straightforward yet effective heuristic that enables the agent to pinpoint hallucination instances, avoid repetition in action sequences, and, in some environments, construct an internal memory map of the given environment. To assess our approach, we evaluate the agent’s ability to complete decision-making tasks in AlfWorld environments and knowledge-intensive, search-based question-and-answer tasks in HotPotQA environments. We observe success rates of 97% and 51%, respectively, and provide a discussion on the emergent property of self-reflection.

r/MachineLearning - [R] Reflexion: an autonomous agent with dynamic memory and self-reflection - Noah Shinn et al 2023 Northeastern University Boston - Outperforms GPT-4 on HumanEval accuracy (0.67 --> 0.88)!
r/MachineLearning - [R] Reflexion: an autonomous agent with dynamic memory and self-reflection - Noah Shinn et al 2023 Northeastern University Boston - Outperforms GPT-4 on HumanEval accuracy (0.67 --> 0.88)!
r/MachineLearning - [R] Reflexion: an autonomous agent with dynamic memory and self-reflection - Noah Shinn et al 2023 Northeastern University Boston - Outperforms GPT-4 on HumanEval accuracy (0.67 --> 0.88)!
r/MachineLearning - [R] Reflexion: an autonomous agent with dynamic memory and self-reflection - Noah Shinn et al 2023 Northeastern University Boston - Outperforms GPT-4 on HumanEval accuracy (0.67 --> 0.88)!