Boosting Search Engines with Interactive Agents

Adolphs, Leonard; Boerschinger, Benjamin; Buck, C.; Huebscher, Michelle Chen; Ciaramita, Massimiliano; Espeholt, Lasse; Hofmann, Thomas; Kilcher, Yannic; Rothe, Sascha; Sessa, Pier Giuseppe; Saralegui, Lierni Sestorain

doi:10.48550/arxiv.2109.00527

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…In the first example, the human decides to search again after removing 'inches', 'width', 'height', and 'white' from the query since product texts often contain abbreviated symbols for these terms like '"', 'w', and 'h'. Thus, search generation is challenging for models since it involves reasoning and adapting to grounded environments, and ideas from query reformulation [37,1] could help alleviate this. Agents also struggle to perform robust semantic matching, which is important in choosing options that contain noisy paraphrases of instruction spans.…”

Section: Discussionmentioning

confidence: 99%

“…Leveraging the web for traditional NLP tasks. Several papers have explored the use of the web for information extraction [34] and retrieval [1], question answering [57,25], dialog [45], and training language models on webtext [2]. These approaches primarily use web search engines as a knowledge retriever for gathering additional evidence for the task at hand.…”

Section: Related Workmentioning

confidence: 99%

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WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Yao¹,

Chen²,

Yang³

et al. 2022

Preprint

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Existing benchmarks for grounding language in interactive environments either lack real-world linguistic elements, or prove difficult to scale up due to substantial human involvement in the collection of data or feedback signals. To bridge this gap, we develop WebShop -a simulated e-commerce website environment with 1.18 million real-world products and 12, 087 crowd-sourced text instructions. Given a text instruction specifying a product requirement, an agent needs to navigate multiple types of webpages and issue diverse actions to find, customize, and purchase an item. WebShop provides several challenges for language grounding including understanding compositional instructions, query (re-)formulation, comprehending and acting on noisy text in webpages, and performing strategic exploration. We collect over 1, 600 human demonstrations for the task, and train and evaluate a diverse range of agents using reinforcement learning, imitation learning, and pre-trained image and language models. Our best model achieves a task success rate of 29%, which outperforms rule-based heuristics (9.6%) but is far lower than human expert performance (59%). We also analyze agent and human trajectories and ablate various model components to provide insights for developing future agents with stronger language understanding and decision making abilities. Finally, we show that agents trained on WebShop exhibit non-trivial sim-to-real transfer when evaluated on amazon.com and eaby.com, indicating the potential value of WebShop in developing practical web-based agents that can operate in the wild. * Equal contribution. Project site with code, data, and demos: https://webshop-pnlp.github.io.Preprint. Under review.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Yao¹,

Chen²,

Yang³

et al. 2022

Preprint

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“…HTML Understanding Autonomous web navigation has been a popular application for neural network models, and a variety of works propose simulated websites for training web-based agents, with application to task fulfillment (Yao et al, 2022;Gur et al, 2021;Burns et al, 2022;Mazumder and Riva, 2020;Shi et al, 2017;Liu et al, 2018) as well as information retrieval or question-answering (Adolphs et al, 2021;Nogueira and Cho, 2016). Simulated websites provide an easy way to evaluate models online, and for this reason we use the existing MiniWoB benchmark (Shi et al, 2017) for our web navigation setting.…”

Section: Related Workmentioning

confidence: 99%

Understanding HTML with Large Language Models

Gur,

Nachum,

Miao

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. Yet, their capabilities for HTML understanding -i.e., parsing the raw HTML of a webpage, with applications to automation of web-based tasks, crawling, and browser-assisted retrieval -have not been fully explored. We contribute HTML understanding models (fine-tuned LLMs) and an indepth analysis of their capabilities under three tasks: (i) Semantic Classification of HTML elements, (ii) Description Generation for HTML inputs, and (iii) Autonomous Web Navigation of HTML pages. While previous work has developed dedicated architectures and training procedures for HTML understanding, we show that LLMs pretrained on standard natural language corpora transfer remarkably well to HTML understanding tasks. For instance, when fine-tuned on data from the MiniWoB benchmark, LLMs successfully complete 50% more tasks using 192x less data compared to the previous best supervised model. We create and open-source a large-scale HTML dataset distilled and auto-labeled from Com-monCrawl. 1

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“…They suggest web-level QA (like WebGPT) as a direction for future work. Adolphs et al [2021] set up an RL problem that involves performing a series of search queries for short-form question-answering. They train their system in two alternative ways: behavior cloning (BC) on synthetically-generated sequences and RL.…”

Section: Related Workmentioning

confidence: 99%

WebGPT: Browser-assisted question-answering with human feedback

Nakano¹,

Hilton²,

Balaji³

et al. 2021

Preprint

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We fine-tune GPT-3 to answer long-form questions using a text-based webbrowsing environment, which allows the model to search and navigate the web. By setting up the task so that it can be performed by humans, we are able to train models on the task using imitation learning, and then optimize answer quality with human feedback. To make human evaluation of factual accuracy easier, models must collect references while browsing in support of their answers. We train and evaluate our models on ELI5, a dataset of questions asked by Reddit users. Our best model is obtained by fine-tuning GPT-3 using behavior cloning, and then performing rejection sampling against a reward model trained to predict human preferences. This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit.

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Boosting Search Engines with Interactive Agents

Cited by 3 publications

References 20 publications

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Understanding HTML with Large Language Models

WebGPT: Browser-assisted question-answering with human feedback

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