A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis

Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web navigation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of inductive bias on HTML. We introduce WebAgent, an LLM-driven agent that can complete the tasks on real websites following natural language instructions. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites via generated Python programs from those. We design WebAgent with Flan-U-PaLM, for grounded code generation, and HTML-T5, new pre-trained LLMs for long HTML documents using local and global attention mechanisms and a mixture of long-span denoising objectives, for planning and summarization. We empirically demonstrate that our recipe improves the success on a real website by over 50%, and that HTML-T5 is the best model to solve HTML-based tasks; achieving 14.9% higher success rate than prior SoTA on the MiniWoB web navigation benchmark and better accuracy on offline task planning evaluation

Understanding HTML with Large Language Models

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 in-depth 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, fine-tuned LLMs are 12% more accurate at semantic classification compared to models trained exclusively on the task dataset. Moreover, 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. Out of the LLMs we evaluate, we show evidence that T5-based models are ideal due to their bidirectional encoder-decoder architecture. To promote further research on LLMs for HTML understanding, we create and open-source a large-scale HTML dataset distilled and auto-labeled from CommonCrawl

Personality Traits in Large Language Models

The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text. As LLMs increasingly power conversational agents used by the general public world-wide, the synthetic personality embedded in these models, by virtue of training on large amounts of human data, is becoming increasingly important. Since personality is a key factor determining the effectiveness of communication, we present a comprehensive method for administering and validating personality tests on widely-used LLMs, as well as for shaping personality in the generated text of such LLMs. Applying this method, we found: 1) personality measurements in the outputs of some LLMs under specific prompting configurations are reliable and valid; 2) evidence of reliability and validity of synthetic LLM personality is stronger for larger and instruction fine-tuned models; and 3) personality in LLM outputs can be shaped along desired dimensions to mimic specific human personality profiles. We discuss application and ethical implications of the measurement and shaping method, in particular regarding responsible AI

Biological Flora of the British Isles: Sorbus torminalis

1.This account presents information on all aspects of the biology of Sorbus torminalis (L.) Crantz (Wild Service-tree) that are relevant to understanding its ecological characteristics and behaviour. The main topics are presented within the standard framework of the Biological Flora of the British Isles: distribution, habitat, communities, responses to biotic factors, responses to environment, structure and physiology, phenology, floral and seed characters, herbivores and disease, history, and conservation.2.Sorbus torminalis is an uncommon, mostly small tree (but can reach 33 m) native to lowland England and Wales, and temperate and Mediterranean regions of mainland Europe. It is the most shade-tolerant member of the genus in the British Isles and as a result it is more closely associated with woodland than any other British species. Like other British Sorbus species, however, it grows best where competition for space and sunlight is limited. Seedlings are shade tolerant but adults are only moderately so. This, combined with its low competitive ability, restricts the best growth to open areas. In shade, saplings and young adults form a sapling bank, showing reproduction and extensive growth only when released. Sorbus torminalis tolerates a wide range of soil reaction (pH 3.5-8.0) but grows best on calcareous clays and thin soils over limestone.3.Sorbus torminalis is a sexual, diploid, non-apomictic species that has hybridised with a number of other Sorbus species to form microspecies. The hermaphrodite flowers are primarily insect pollinated. Seed production is reliable only in warm years, especially at the edge of its range, although even then seed viability is low. The fruits are primarily dispersed by carnivorous mammals. Seeds display embryo dormancy but most will germinate the first spring after falling.4.This tree is very tolerant of short droughts but only moderately tolerant of frost, hence its southerly and lowland distribution. It faces no particular individual threats although the small size of most populations makes it susceptible to habitat loss and fragmentation, particularly through the loss of open coppiced areas. As a consequence it appears to be declining throughout Britain and Europe despite its wide range of historical uses and the high value of its timber. The extent to which these losses will be offset by increases due to climate change is unknown.This article is protected by copyright. All rights reserved

WPA2 security-bandwith trade-off in 802.11n peer-peer WLAN for IPv4 and IPv6 using Windows XP and Windows 7 operating systems

In this paper, we present new results on the performance of IEEE 802.11n using open system (no security) and WPA2 security for Windows XP and Windows 7. Enabling WPA2 security results in approximately 4.4 Mbps less TCP throughput than open system for both IPv4 and IPv6 on Windows XP and up to 2.8 Mbps less TCP throughput for Windows 7. For both open system and WPA2 security, Windows 7 provides higher IPv4 and IPv6 bandwidth than Windows XP and IPv4 provides higher bandwidth than IPv6

Effect of WPA2 Security on IEEE 802.11n bandwidth and round trip time in peer-peer wireless local area networks

In this paper 802.11 wireless peer-peer network is evaluated for both IPv4 and IPv6 in Windows 7 and Fedora 12 operating systems. IPv4 has higher throughput than IPv6 for all packet sizes for both Windows 7 and Fedora 12 operating systems. Results further indicate that implementing WPA2 wireless security reduces bandwidth and increase delay in wireless networks

Photo-catalytic pretreatment of biomass for anaerobic digestion using visible light and Nickle oxide (NiOx) nanoparticles prepared by sol gel method

International audienc

Screening of native hyper-lipid producing microalgae strains for biomass and lipid production

International audienc