Autonomous agents that address day-to-day digital tasks (e.g., ordering groceries for a household), must not only operate multiple apps (e.g., notes, messaging, shopping app) via APIs, but also generate rich code with complex control flow in an iterative manner based on their interaction with the environment. However, existing benchmarks for tool use are inadequate, as they only cover tasks that require a simple sequence of API calls. To remedy this gap, we built AppWorld Engine, a high-quality execution environment (60K lines of code) of 9 day-to-day apps operable via 457 APIs and populated with realistic digital activities simulating the lives of ~100 fictitious users. We then created AppWorld Benchmark (40K lines of code), a suite of 750 natural, diverse, and challenging autonomous agent tasks requiring rich and interactive code generation. It supports robust programmatic evaluation with state-based unit tests, allowing for different ways of completing a task while also checking for unexpected changes, i.e., collateral damage. The state-of-the-art LLM, GPT4O, solves only ~49% of our ‘normal’ tasks and ~30% of ‘challenge’ tasks, while other models solve at least 16% fewer. This highlights the benchmark’s difficulty and AppWorld’s potential to push the frontiers of interactive coding agents.

In this paper, we study the applicability of Bayesian Parametric and Non-parametric methods for user clustering in an E-commerce search setting. To the best of our knowledge, this is the first work that presents a comparative study of various Bayesian clustering methods in the context of product search. Specifically, we cluster users based on their topical patterns from their respective product search queries. To evaluate the quality of the clusters formed, we perform a collaborative query recommendation task. Our findings indicate that simple parametric model like Latent Dirichlet Allocation (LDA) outperforms more sophisticated non-parametric methods like Distance Dependent Chinese Restaurant Process and Dirichlet Process-based clustering in both tasks.

This paper describes our efforts in tackling Task 5 of SemEval-2020. The task involved detecting a class of textual expressions known as counterfactuals and separating them into their constituent elements. Our final submitted approaches were an ensemble of various fine-tuned transformer-based and CNN-based models for the first subtask and a transformer model with dependency tree information for the second subtask. We ranked 4-th and 9-th in the overall leaderboard. We also explored various other approaches that involved classical methods, other neural architectures and incorporation of different linguistic features.

The purpose of this research is to address the problem of extracting information from travel itineraries and discuss the challenges faced in the process. Business-to-customer emails like booking confirmations and e-tickets are usually machine generated by filling slots in pre-defined templates which improve the presentation of such emails but also make the emails more complex in structure. Extracting the relevant information from these emails would let users track their journeys and important updates on applications installed on their devices to give them a consolidated over view of their itineraries and also save valuable time. We investigate the use of an HMM-based named entity recognizer on such emails which we will use to label and extract relevant entities. NER in such emails is challenging as these itineraries offer less useful contextual information. We also propose a rich set of features which are integrated into the model and are specific to our domain. The result from our model is a list of lists containing the relevant information extracted from ones itinerary.