5 papers · 1 filter
Does The Way You Plan Matter? An Empirical Study of Planning Representations for LLM Web Agents
Alejandra Zambrano, Sara Vera Marjanovic, Imene Kerboua +2
Despite recent advances, LLM-based web agents still struggle with limited exploration, omission of critical steps, and sensitivity to task constraints. Prior work suggests that man…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of Web Agents
Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar +7
Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens of thousands of tokens. This sat…
LineRetriever: Planning-Aware Observation Reduction for Web Agents
Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar +6
While large language models have demonstrated impressive capabilities in web navigation tasks, the extensive context of web pages, often represented as DOM or Accessibility Tree (A…
Maintaining MTEB: Towards Long Term Usability and Reproducibility of Embedding Benchmarks
Isaac Chung, Imene Kerboua, Marton Kardos +2
The Massive Text Embedding Benchmark (MTEB) has become a standard evaluation platform for text embedding models. While previous work has established the core benchmark methodology,…