New Open Long-Context LLM; LLMs For Text Analysis; Graph-2-Text Generative Models; Fine-Tune Your Own Llama 2; and More

Editor's Paper Recommendations

Evaluating Generative Models for Graph-to-Text Generation: This paper explores using generative models for graph-to-text generation tasks in a zero-shot setting. GPT-3 and ChatGPT are evaluated and compared to finetuned LLMs like T5 and BART on two datasets. Results show that generative models can produce fluent text with BLEU scores of 10.57 and 11.08 for AGENDA and WebNLG datasets, respectively. However, the error analysis reveals challenges in understanding semantic relations between entities and generating text with hallucinations or irrelevant information. BERT is used for machine-generated text detection, achieving high macro-F1 scores. The text generated by generative models is publicly available.

An Overview Of Temporal Commonsense Reasoning and Acquisition: Temporal commonsense reasoning is the ability to understand the typical temporal context of phrases, actions, and events and use it to solve problems. It is crucial for temporal natural language processing tasks like timeline summarization and temporal question answering. Recent research suggests that large language models perform well in generating sentences and classifications but need help with reasoning and fall into linguistic traps. This article provides an overview of research on enhancing language model performance through augmentations and evaluation of various datasets. However, even with these improvements, models still need help to match human performance in reasoning tasks related to temporal common-sense properties. The need for careful interpretation of research and suitable evaluation metrics is emphasized to avoid overpromising results due to the shallow reasoning present in transformers.

How to use LLMs for Text Analysis: This guide introduces Large Language Models (LLM) as a versatile text analysis method in the social sciences. LLMs are easy-to-use, cost-effective, and fast, making them applicable to various text analysis tasks, such as annotation, classification, sentiment analysis, and critical discourse analysis. It targets students and researchers with limited programming experience, providing a simple introduction to using LLMs for text analysis in their research projects, along with best practices. The guide covers the entire process, including installing the required software, setting up the API, loading data, developing an analysis prompt, performing the text analysis, and validating the results. It uses the example of identifying populism in political texts to demonstrate how LLMs surpass the existing state-of-the-art methods.

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models: The paper introduces OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replication of DeepMind's Flamingo models. OpenFlamingo models average between 80 - 89% of corresponding Flamingo performance on seven vision-language datasets. This technical report describes our models, training data, hyperparameters, and evaluation suite. We share our models and code at this https URL.

Industry Insights

1.  Llama 2 is the Best Open Source of LLM so Far
2. Fine-Tune Your Own Llama 2 Model in a Colab Notebook
3. ChatGPT gets several new features, including multi-document chat
4. Abacus AI Introduces A New Open Long-Context Large Language Model LLM: Meet Giraffe
5. How to use Llama 2 with Python to build AI projects

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• How to build a sample recommendation engine or app using OpenAI and SingleStore by utilizing JSON data within your enterprise
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• SingleStoreDB's scalable, distributed architecture and OpenAI's machine learning models for generative AI.

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