Matthew Toles

Matthew Toles, Yukun Huang, Zhou Yu et al.

Real-life tasks such as giving legal or technical advice often lack complete context at the outset and can have disparate answers depending thereon. The ability to derive missing factual information by asking clarifying questions (ACQ) is an important element of real-life collaboration on such reasoning tasks. Existing factual clarification question challenges evaluate generations based on word overlap or human evaluations. Recent work explores generating a response to the clarifying question then evaluating its utility directly. So far, these tasks are limited to disambiguating the user's intent rather than concrete facts about the situation. The factual domain presents unique challenges since responses to clarification questions must be factually true for accurate evaluation. To enable evaluation of factual domain clarification question generation, We present a new task that focuses on the ability to elicit missing information in multi-hop reasoning tasks. The task, HotpotQA-FLM, can be evaluated automatically, making it convenient for benchmarking language models. We observe that humans outperform GPT-4 by a large margin, while Llama 3 8B Instruct does not even beat the dummy baseline in some metrics. Finally, we find by fine-tuning Llama 3 8B Instruct on its own generations, filtered via rejection sampling, we can improve information recovery by 27.6 percent.

Matthew Toles, Rattandeep Singh, Isaac Song Zhou Yu

Completing paperwork is a challenging and time-consuming problem. Form filling is especially challenging in the pure-image domain without access to OCR, typeset PDF text, or a DOM. For computer agents, it requires multiple abilities, including multi-modal understanding, information retrieval, and tool-use. We present a novel form-filling benchmark consisting of 432 fields spread across 55 documents and 3 tasks, requiring knowledge of 236 features per user. We find that baseline VLAs achieve less than 1% accuracy in most cases, primarily due to poor localization ability. GUI agents also struggle, scoring between 10.6-68.0% despite high cost and latency. Therefore, we also contribute FieldFinder, a tool to assist LLMs in identifying where to place text on a form. With FieldFinder, all models achieve equal or better performance in all six study conditions, with a maximum increase from 2% to 56%.

Matthew Toles, Nikhil Balwani, Rattandeep Singh et al.

Many real-world eligibility problems, ranging from medical diagnosis to tax planning, can be mapped to decision problems expressed in natural language, wherein a model must make a binary choice based on user features. Large-scale domains such as legal codes or frequently updated funding opportunities render human annotation (e.g., web forms or decision trees) impractical, highlighting the need for agents that can automatically assist in decision-making. Since relevant information is often only known to the user, it is crucial that these agents ask the right questions. As agents determine when to terminate a conversation, they face a trade-off between accuracy and the number of questions asked, a key metric for both user experience and cost. To evaluate this task, we propose BeNYfits, a new benchmark for determining user eligibility for multiple overlapping social benefits opportunities through interactive decision-making. Our experiments show that current language models struggle with frequent hallucinations, with GPT-4o scoring only 35.7 F1 using a ReAct-style chain-of-thought. To address this, we introduce ProADA, a novel approach that leverages program synthesis to assist in decision-making by mapping dialog planning to a code generation problem and using gaps in structured data to determine the best next action. Our agent, ProADA, improves the F1 score to 55.6 while maintaining nearly the same number of dialog turns.