Generative Representational Instruction Tuning

• URL: http://arxiv.org/abs/2402.09906v2
• Date: Wed, 17 Apr 2024 17:12:05 GMT
• Title: Generative Representational Instruction Tuning
• Authors: Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, Douwe Kiela,
• Abstract summary: GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) GritLM 8x7B outperforms all open generative language models that we tried while still being among the best embedding models.
• Score: 89.76840377003178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8x7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.

Related papers

• RAGViz: Diagnose and Visualize Retrieval-Augmented Generation [16.91653397201039]
  Retrieval-augmented generation (RAG) combines knowledge from domain-specific sources into large language models. We propose RAGViz, a RAG diagnosis tool that visualizes the attentiveness of the generated tokens in retrieved documents. RAGViz provides two main functionalities: (1) token and document-level attention visualization, and (2) generation comparison upon context document addition and removal.
  arXiv  Detail & Related papers  (2024-11-04T02:30:05Z)
• Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA [51.3033125256716]
  We model the subgraph retrieval task as a conditional generation task handled by small language models. Our base generative subgraph retrieval model, consisting of only 220M parameters, competitive retrieval performance compared to state-of-the-art models. Our largest 3B model, when plugged with an LLM reader, sets new SOTA end-to-end performance on both the WebQSP and CWQ benchmarks.
  arXiv  Detail & Related papers  (2024-10-08T15:22:36Z)
• ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning [72.90823351726374]
  We introduce the Unified framework for Large Language Model Embedding (ULLME), a flexible, plug-and-play implementation that enables bidirectional attention across various LLMs. We also propose Generation-augmented Representation Learning (GRL), a novel fine-tuning method to boost LLMs for text embedding tasks. To showcase our framework's flexibility and effectiveness, we release three pre-trained models from ULLME with different backbone architectures.
  arXiv  Detail & Related papers  (2024-08-06T18:53:54Z)
• Evaluating Generative Models for Graph-to-Text Generation [0.0]
  We explore the capability of generative models to generate descriptive text from graph data in a zero-shot setting. Our results demonstrate that generative models are capable of generating fluent and coherent text. However, our error analysis reveals that generative models still struggle with understanding the semantic relations between entities.
  arXiv  Detail & Related papers  (2023-07-27T09:03:05Z)
• Generate to Understand for Representation [3.5325087487696463]
  GUR is a pretraining framework that combines language modeling and contrastive learning objectives in a single training step. GUR achieves impressive results without any labeled training data, outperforming all other pretrained baselines as a retriever at the recall benchmark in a zero-shot setting.
  arXiv  Detail & Related papers  (2023-06-14T06:00:18Z)
• DORE: Document Ordered Relation Extraction based on Generative Framework [56.537386636819626]
  This paper investigates the root cause of the underwhelming performance of the existing generative DocRE models. We propose to generate a symbolic and ordered sequence from the relation matrix which is deterministic and easier for model to learn. Experimental results on four datasets show that our proposed method can improve the performance of the generative DocRE models.
  arXiv  Detail & Related papers  (2022-10-28T11:18:10Z)
• Multitask Prompted Training Enables Zero-Shot Task Generalization [70.12770442071657]
  We develop a system for mapping general natural language tasks into a human-readable prompted form. We fine-tune a pretrained encoder-decoder model on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models 16x its size.
  arXiv  Detail & Related papers  (2021-10-15T17:08:57Z)
• KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation [100.79870384880333]
  We propose a knowledge-grounded pre-training (KGPT) to generate knowledge-enriched text. We adopt three settings, namely fully-supervised, zero-shot, few-shot to evaluate its effectiveness. Under zero-shot setting, our model achieves over 30 ROUGE-L on WebNLG while all other baselines fail.
  arXiv  Detail & Related papers  (2020-10-05T19:59:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.