Portrait of Alejandra Zambrano is unavailable

Alejandra Zambrano

Collaborating Alumni - McGill University
Supervisor
Siva Reddy
Research Topics
Large Language Models (LLM)
Natural Language Processing
Representation Learning
GitHub

Publications

AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories
Xing Han Lu
Amirhossein Kazemnejad
Nicholas Meade
Arkil Patel
Dongchan Shin
Alejandra Zambrano
Karolina Stanczak
Peter Shaw
Chris Pal
Siva Reddy
Web agents enable users to perform tasks on web browsers through natural language interaction. Evaluating web agents trajectories is an impo… (see more)rtant problem, since it helps us determine whether the agent successfully completed the tasks. Rule-based methods are widely used for this purpose, but they are challenging to extend to new tasks and may not always recognize successful trajectories. We may achieve higher accuracy through human evaluation, but the process would be substantially slower and more expensive. Automatic evaluations with LLMs may avoid the challenges of designing new rules and manually annotating trajectories, enabling faster and cost-effective evaluation. However, it is unclear how effective they are at evaluating web agents. To this end, we propose AgentRewardBench, the first benchmark to assess the effectiveness of LLM judges for evaluating web agents. AgentRewardBench contains 1302 trajectories across 5 benchmarks and 4 LLMs. Each trajectory in AgentRewardBench is reviewed by an expert, who answers questions pertaining to the success, side effects, and repetitiveness of the agent. Using our benchmark, we evaluate 12 LLM judges and find that no single LLM excels across all benchmarks. We also find that the rule-based evaluation used by common benchmarks tends to underreport the success rate of web agents, highlighting a key weakness of rule-based evaluation and the need to develop more flexible automatic evaluations. We release the benchmark at: https://agent-reward-bench.github.io
2025-07-07
colmweb.org/COLM/2025/Conference (accepted)
doi.org
openreview.net
SafeArena: Evaluating the Safety of Autonomous Web Agents
Ada Defne Tur
Nicholas Meade
Xing Han Lu
Alejandra Zambrano
Arkil Patel
Esin DURMUS
Spandana Gella
Karolina Stanczak
Siva Reddy
2025-05-01
ICML.cc/2025/Conference (poster)
doi.org
openreview.net
SafeArena: Evaluating the Safety of Autonomous Web Agents
Ada Defne Tur
Nicholas Meade
Xing Han Lu
Alejandra Zambrano
Arkil Patel
Esin DURMUS
Spandana Gella
Karolina Sta'nczak
Siva Reddy
2025-03-06
ArXiv (preprint)
arxiv.org
arxiv.org
SafeArena: Evaluating the Safety of Autonomous Web Agents
Ada Defne Tur
Nicholas Meade
Xing Han Lu
Alejandra Zambrano
Arkil Patel
Esin DURMUS
Spandana Gella
Karolina Stanczak
Siva Reddy
LLM-based agents are becoming increasingly proficient at solving web-based tasks. With this capability comes a greater risk of misuse for ma… (see more)licious purposes, such as posting misinformation in an online forum or selling illicit substances on a website. To evaluate these risks, we propose SafeArena, the first benchmark to focus 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. Our benchmark is available here: https://safearena.github.io
2025-03-06
ArXiv (preprint)
doi.org
arxiv.org