Mingyang Zhang
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Learning to Rewrite Prompts for Personalized Text Generation
Qiaozhu Mei
Proceedings of the ACM Web Conference 2024
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Facilitated by large language models (LLMs), personalized text generation has become a rapidly growing research direction. Most existing studies focus on designing specialized models for a particular domain, or they require fine-tuning the LLMs to generate personalized text. We consider a typical scenario in which the large language model, which generates personalized output, is frozen and can only be accessed through APIs. Under this constraint, all one can do is to improve the input text (i.e., text prompts) sent to the LLM, a procedure that is usually done manually. In this paper, we propose a novel method to automatically revise prompts for personalized text generation. The proposed method takes the initial prompts generated by a state-of-the-art, multistage framework for personalized generation and rewrites a few critical components that summarize and synthesize the personal context. The prompt rewriter employs a training paradigm that chains together supervised learning (SL) and reinforcement learning (RL), where SL reduces the search space of RL and RL facilitates end-to-end training of the rewriter. Using datasets from three representative domains, we demonstrate that the rewritten prompts outperform both the original prompts and the prompts optimized via supervised learning or reinforcement learning alone. In-depth analysis of the rewritten prompts shows that they are not only human readable, but also able to guide manual revision of prompts when there is limited resource to employ reinforcement learning to train the prompt rewriter, or when it is costly to deploy an automatic prompt rewriter for inference.
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WSDM 2024 Workshop on Large Language Models for Individuals, Groups, and Society
Qiaozhu Mei
Vanessa Murdock
Jie Tang
Hongning Wang
Hamed Zamani
(2024) (to appear)
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This workshop discusses the cutting-edge developments in research and applications of personalizing large language models (LLMs) and adapting them to the demands of diverse user populations and societal needs. The full-day workshop plan includes several keynotes and invited talks, a poster session and a panel discussion.
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PRewrite: Prompt Rewriting with Reinforcement Learning
Qiaozhu Mei
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024) (to appear)
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Prompt engineering is critical for the development of LLM-based applications. However, it is usually done manually in a "trial and error" fashion that can be time consuming, ineffective, and sub-optimal. Even for the prompts which seemingly work well, there is always a lingering question: can the prompts be made better with further modifications?
To address these problems, we investigate automated prompt engineering in this paper. Specifically, we propose PRewrite, an automated method to rewrite an under-optimized prompt to a more effective prompt. We instantiate the prompt rewriter using an LLM. The rewriter LLM is trained using reinforcement learning to optimize the performance on a given downstream task. We conduct experiments on diverse benchmark datasets, which demonstrates the effectiveness of PRewrite.
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Bridging the Preference Gap between Retrievers and LLMs
Zixuan Ke
Qiaozhu Mei
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024) (to appear)
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Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM. However, the relationship between retrievers and LLM in a RAG is still under-investigated. Most existing work treats the retriever and the LLM as independent components and leaves a gap between retrieving human-"friendly" information and assembling a LLM-"friendly" context. In this work, we examine a novel bridge mechanism. We validate the ranking and selection assumptions of retrievers in the context of RAG and propose a framework that chains together supervised and reinforcement learning to train a bridge model that optimizes the connection between the retriever and the LLM. Empirical results demonstrate the effectiveness of our method in both question-answering and personalized generation tasks.
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Job Type Extraction for Service Businesses
Yaping Qi
Hayk Zakaryan
Yonghua Wu
Companion Proceedings of the ACM Web Conference 2023
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Google My Business (GMB) is a platform that allows business owners to manage their business profiles, which will be displayed when a user issues a relevant query on Google Search or Maps. Many GMB businesses provide diverse services from home cleaning and plumbing to legal services and education. However the exact service content, which we call job types, is often missing in their profiles. This leaves the burden of finding such content to the users, either by the tedious work of scanning through business websites or time-consuming calling of the owners. In the present paper, we describe how we build a pipeline to automatically extract the job types from websites of business owners and how we solve scalability issues for deployment. Rather than focusing on developing novel and sophisticated machine learning models, we share various challenges we have faced and practical experiences of building such a pipeline, including the cold start problem of dataset collection with limited human annotation resource, scalability, reaching a launch bar of high precision, and building a general pipeline with reasonable coverage of any free-text web pages without relying on the Document Object Model (DOM) structure. With these challenges, standard approaches for information extraction do not directly apply or are not scalable to be served. In this paper, we show how we address these challenges in different stages of the extraction pipeline, including: (1) utilizing structured content like tables and lists to tackle the cold start problem of dataset collection; (2) exploitation of various context information to improve model performance without hurting scalability; and (3) formulating the extraction problem as a retrieval task to improve generalizability, efficiency as well as coverage. The pipeline has been successfully deployed, and is scalable enough to be refreshed every few days to extract the latest online information. The extracted job types are serving millions of users of Google Search and Google Maps with at least three use cases: (1) job types of a place are directly displayed on mobile devices; (2) job types provide explanation as to why a place shows up given a query; (3) job types are used as a signal to rank business places. According to a user survey, the displayed job types has greatly enhanced the probability of a user hiring a service provider.
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Learning Sparse Lexical Representations Over Expanded Vocabularies for Retrieval
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23) (2023)
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A recent line of work in first-stage Neural Information Retrieval has focused on learning sparse lexical representations instead of dense embeddings.
One such work is SPLADE, which has been shown to lead to state-of-the-art results in both the in-domain and zero-shot settings, can leverage inverted indices for efficient retrieval, and offers enhanced interpretability.
However, existing SPLADE models are fundamentally limited to learning a sparse representation based on the native BERT WordPiece vocabulary.
In this work, we extend SPLADE to support learning sparse representations over arbitrary sets of tokens to improve flexibility and aid integration with existing retrieval systems.
As an illustrative example, we focus on learning a sparse representation over a large (300k) set of unigrams.
We add an unsupervised pretraining task on C4
to learn internal representations for new tokens.
Our experiments show that our Expanded-SPLADE model maintains the performance of WordPiece-SPLADE on both in-domain and zero-shot retrieval while allowing for custom output vocabularies.
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End-to-End Query Term Weighting
Karan Samel
Swaraj Khadanga
Wensong Xu
Xingyu Wang
Kashyap Kolipaka
Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '23) (2023)
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Bag-of-words based lexical retrieval systems are still the most commonly used methods for real-world search applications. Recently deep learning methods have shown promising results to improve this retrieval performance but are expensive to run in an online fashion, non-trivial to integrate into existing production systems, and might not generalize well in out-of-domain retrieval scenarios. Instead, we build on top of lexical retrievers by proposing a Term Weighting BERT (TW-BERT) model. TW-BERT learns to predict the weight for individual n-gram (e.g., uni-grams and bi-grams) query input terms. These inferred weights and terms can be used directly by a retrieval system to perform a query search. To optimize these term weights, TW-BERT incorporates the scoring function used by the search engine, such as BM25, to score query-document pairs. Given sample query-document pairs we can compute a ranking loss over these matching scores, optimizing the learned query term weights in an end-to-end fashion. Aligning TW-BERT with search engine scorers minimizes the changes needed to integrate it into existing production applications, whereas existing deep learning based search methods would require further infrastructure optimization and hardware requirements. The learned weights can be easily utilized by standard lexical retrievers and by other retrieval techniques such as query expansion. We show that TW-BERT improves retrieval performance over strong term weighting baselines within MSMARCO and in out-of-domain retrieval on TREC datasets.
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Creator Context for Tweet Recommendation
Matt Colen
Sergey Levi
Vladimir Ofitserov
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
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When discussing a tweet, people usually not only refer to the content it delivers, but also to the person behind the tweet. In other words, grounding the interpretation of the tweet in the context of its creator plays an important role in deciphering the true intent and the importance of the tweet.
In this paper, we attempt to answer the question of how creator context should be used to advance tweet understanding. Specifically, we investigate the usefulness of different types of creator context, and examine different model structures for incorporating creator context in tweet modeling. We evaluate our tweet understanding models on a practical use case -- recommending relevant tweets to news articles. This use case already exists in popular news apps, and can also serve as a useful assistive tool for journalists. We discover that creator context is essential for tweet understanding, and can improve application metrics by a large margin. However, we also observe that not all creator contexts are equal. Creator context can be time sensitive and noisy. Careful creator context selection and deliberate model structure design play an important role in creator context effectiveness.
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SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23), ACM (2023) (to appear)
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In dense retrieval, prior work has largely improved retrieval effectiveness using multi-vector dense representations, exemplified by ColBERT. In sparse retrieval, more recent work, such as SPLADE, demonstrated that one can also learn sparse lexical representations to achieve comparable effectiveness while enjoying better interpretability. In this work, we combine the strengths of both the sparse and dense representations for first-stage retrieval. Specifically, we propose SparseEmbed – a novel retrieval model that learns sparse lexical representations with contextual embeddings. Compared with SPLADE, our model leverages the contextual embeddings to improve model expressiveness. Compared with ColBERT, our sparse representations are trained end-to-end to optimize both efficiency and effectiveness.
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Multi-Aspect Dense Retrieval
Swaraj Khadanga
Wensong Xu
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM (2022)
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Prior work in Dense Retrieval usually encodes queries and documents using single-vector representations (also called embeddings) and performs retrieval in the embedding space using approximate nearest neighbor search. This paradigm enables efficient semantic retrieval. However, the single-vector representations can be ineffective at capturing different aspects of the queries and documents in relevance matching, especially for some vertical domains. For example, in e-commerce search, these aspects could be category, brand and color. Given a query "white nike socks", a Dense Retrieval model may mistakenly retrieve some "white adidas socks" while missing out the intended brand. We propose to explicitly represent multiple aspects using one embedding per aspect. We introduce an aspect prediction task to teach the model to capture aspect information with particular aspect embeddings. We design a lightweight network to fuse the aspect embeddings for representing queries and documents. Our evaluation using an e-commerce dataset shows impressive improvements over strong Dense Retrieval baselines. We also discover that the proposed aspect embeddings can enhance the interpretability of Dense Retrieval models as a byproduct.
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