LLM-based agents are becoming increasingly proficient at solving web-based tasks. With this capability comes a greater risk of misuse for malicious purposes, such as posting misinformation in an online forum or selling illicit substances on a website. To evaluate these risks, we propose SafeArena, a benchmark focused on the deliberate misuse of web agents. SafeArena comprises 250 safe and 250 harmful tasks across four websites. We classify the harmful tasks into five harm categories—misinformation, illegal activity, harassment, cybercrime, and social bias, designed to assess realistic misuses of web agents. We evaluate leading LLM-based web agents, including GPT-4o, Claude-3.5 Sonnet, Qwen-2-VL 72B, and Llama-3.2 90B, on our benchmark. To systematically assess their susceptibility to harmful tasks, we introduce the Agent Risk Assessment framework that categorizes agent behavior across four risk levels. We find agents are surprisingly compliant with malicious requests, with GPT-4o and Qwen-2 completing 34.7% and 27.3% of harmful requests, respectively. Our findings highlight the urgent need for safety alignment procedures for web agents.

We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WEBLINX - a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. Our benchmark covers a broad range of patterns on over 150 real-world websites and can be used to train and evaluate agents in diverse scenarios. Due to the magnitude of information present, Large Language Models (LLMs) cannot process entire web pages in real-time. To solve this bottleneck, we design a retrieval-inspired model that efficiently prunes HTML pages by ranking relevant elements. We use the selected elements, along with screenshots and action history, to assess a variety of models for their ability to replicate human behavior when navigating the web. Our experiments span from small text-only to proprietary multimodal LLMs. We find that smaller finetuned decoders surpass the best zero-shot LLMs (including GPT-4V), but also larger finetuned multimodal models which were explicitly pretrained on screenshots. However, all finetuned models struggle to generalize to unseen websites. Our findings highlight the need for large multimodal models that can generalize to novel settings.

Abstract Instruction-following models are attractive alternatives to fine-tuned approaches for question answering (QA). By simply prepending relevant documents and an instruction to their input, these models can be adapted to various information domains and tasks without additional training. However, these models tend to produce verbose responses with supplementary information, which makes traditional QA metrics like exact match (EM) and F1 unreliable for accurately quantifying model performance. In this work, we evaluate instruction-following models along two fronts: 1) how well they satisfy user’s information need (correctness), and 2) whether they disseminate information supported by the provided knowledge (faithfulness). Guided by human evaluation and analysis, we highlight the shortcomings of traditional metrics for both correctness and faithfulness and propose simple token-overlap metrics that correlate highly with human judgments. Our analysis reveals that for correctness, instruction-following models perform comparably to models specifically fine-tuned for that task. However, they struggle to accurately judge the relevance of the provided knowledge and often hallucinate in their responses. We hope our work encourages more holistic evaluation of instruction-following models for QA. Our code and human annotation data is available at https://github.com/McGill-NLP/instruct-qa.

We introduce the StatCan Dialogue Dataset 1 consisting of 19,379 conversation turns between agents working at Statistics Canada and online users looking for published data tables.The conversations stem from genuine intents, are held in English or French, and lead to agents retrieving one of over 5000 complex data tables.Based on this dataset, we propose two tasks: (1) automatic retrieval of relevant tables based on a on-going conversation, and (2) automatic generation of appropriate agent responses at each turn.We investigate the difficulty of each task by establishing strong baselines.Our experiments on a temporal data split reveal that all models struggle to generalize to future conversations, as we observe a significant drop in performance across both tasks when we move from the validation to the test set.In addition, we find that response generation models struggle to decide when to return a table.Considering that the tasks pose significant challenges to existing models, we encourage the community to develop models for our task, which can be directly used to help knowledge workers find relevant tables for live chat users.† Work done as visiting researcher at ServiceNow Research 1 Website: mcgill-nlp.github.io/statcan-dialogue-datasetU1: Hi, I'm looking to obtain quarterly data in regards to GDP grow (Canada), BC Housing STarts, Canada Oil Price/BBL A1: Hello, my name is