Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models
- URL: http://arxiv.org/abs/2409.11136v1
- Date: Tue, 17 Sep 2024 12:42:55 GMT
- Title: Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models
- Authors: Orion Weller, Benjamin Van Durme, Dawn Lawrie, Ashwin Paranjape, Yuhao Zhang, Jack Hessel,
- Abstract summary: We present Promptriever, the first retrieval model able to be prompted like an LM.
Promptriever achieves strong performance on standard retrieval tasks, and also follows instructions.
- Score: 54.272894325370956
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Instruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts. In this work, we present Promptriever, the first retrieval model able to be prompted like an LM. To train Promptriever, we curate and release a new instance-level instruction training set from MS MARCO, spanning nearly 500k instances. Promptriever not only achieves strong performance on standard retrieval tasks, but also follows instructions. We observe: (1) large gains (reaching SoTA) on following detailed relevance instructions (+14.3 p-MRR / +3.1 nDCG on FollowIR), (2) significantly increased robustness to lexical choices/phrasing in the query+instruction (+12.9 Robustness@10 on InstructIR), and (3) the ability to perform hyperparameter search via prompting to reliably improve retrieval performance (+1.4 average increase on BEIR). Promptriever demonstrates that retrieval models can be controlled with prompts on a per-query basis, setting the stage for future work aligning LM prompting techniques with information retrieval.
Related papers
- Modular Prompt Learning Improves Vision-Language Models [49.132774679968456]
We propose Modular Prompt Learning (MPL) to promote the preservation of information contained in the inserted prompts.
On average, our method achieves 0.7% performance gain on the base-to-new generalization task.
The largest improvement on the individual dataset is 10.7%.
arXiv Detail & Related papers (2025-02-19T22:00:20Z) - mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval [61.17793165194077]
We introduce mFollowIR, a benchmark for measuring instruction-following ability in retrieval models.
We present results for both multilingual (XX-XX) and cross-lingual (En-XX) performance.
We see strong cross-lingual performance with English-based retrievers that trained using instructions, but find a notable drop in performance in the multilingual setting.
arXiv Detail & Related papers (2025-01-31T16:24:46Z) - IPO: Interpretable Prompt Optimization for Vision-Language Models [40.83071220530289]
This paper introduces a simple but interpretable prompt (IPO)
IPO utilizes large language models (LLMs) to generate textual prompts dynamically.
We incorporate a large multimodal model (LMM) to condition on visual content by generating image descriptions.
arXiv Detail & Related papers (2024-10-20T14:10:22Z) - Automatic Prompt Selection for Large Language Models [22.73421169410049]
We propose an effective approach to automatically select the optimal prompt for a given input from a finite set of synthetic candidate prompts.
Our approach balances prompt generality-specificity and eliminates the need for resource-intensive training and inference.
It demonstrates competitive performance on zero-shot question-answering datasets: GSM8K, MultiArithm, and AQuA.
arXiv Detail & Related papers (2024-04-03T13:20:24Z) - Effective Structured Prompting by Meta-Learning and Representative Verbalizer [27.64413828719264]
We propose MetaPrompter for effective structured prompting.
We propose a novel soft verbalizer (RepVerb) which constructs label embedding from feature embeddings directly.
Experimental results demonstrate that MetaPrompter performs better than the recent state-of-the-arts.
arXiv Detail & Related papers (2023-06-01T12:44:33Z) - TEMPERA: Test-Time Prompting via Reinforcement Learning [57.48657629588436]
We propose Test-time Prompt Editing using Reinforcement learning (TEMPERA)
In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge.
Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods.
arXiv Detail & Related papers (2022-11-21T22:38:20Z) - Explaining Patterns in Data with Language Models via Interpretable
Autoprompting [143.4162028260874]
We introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data.
iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions.
Experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery.
arXiv Detail & Related papers (2022-10-04T18:32:14Z) - AdaPrompt: Adaptive Model Training for Prompt-based NLP [77.12071707955889]
We propose AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs.
Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings.
In zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.
arXiv Detail & Related papers (2022-02-10T04:04:57Z)
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.