4 citations · 6 across the 9 of their papers we have counts for
10 papers
SWE-VERIFIER: Selective Verification for Cost-Efficient Coding Agents
Kaijian Zou, Vaibhav Chennareddy, Yunxiang Zhang +5
Coding agents spend substantial computation generating actions and processing long interaction histories. Poor action choices can steer agents into low-value states, wasting comput…
AtlasNLP: A Country-Aware Atlas of Dataset Representation in NLP
Joan Nwatu, Tsedeniya Solomon Amare, Longju Bai +17
Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and informing AI policy. However, ge…
BAGEN: Are LLM Agents Budget-Aware?
Yuxiang Lin, Zihan Wang, Mengyang Liu +9
While agents are increasingly spending more resources, today agent cost is mostly measured only after execution. A Budget-Aware Agent (BAGEN) should treat budget as an active contr…
How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks
Longju Bai, Zhemin Huang, Xingyao Wang +5
The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that require a significant amount of t…
Culture Affordance Atlas: Reconciling Object Diversity Through Functional Mapping
Joan Nwatu, Longju Bai, Oana Ignat +1
Culture shapes the objects people use and for what purposes, yet mainstream Vision-Language (VL) datasets frequently exhibit cultural biases, disproportionately favoring higher-inc…
Chumor 2.0: Towards Benchmarking Chinese Humor Understanding
Ruiqi He, Yushu He, Longju Bai +7
Existing humor datasets and evaluations predominantly focus on English, leaving limited resources for culturally nuanced humor in non-English languages like Chinese. To address thi…
The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning
Longju Bai, Angana Borah, Oana Ignat +1
Large Multimodal Models (LMMs) exhibit impressive performance across various multimodal tasks. However, their effectiveness in cross-cultural contexts remains limited due to the pr…
Why AI Is WEIRD and Should Not Be This Way: Towards AI For Everyone, With Everyone, By Everyone
Rada Mihalcea, Oana Ignat, Longju Bai +7
This paper presents a vision for creating AI systems that are inclusive at every stage of development, from data collection to model design and evaluation. We address key limitatio…
Chumor 1.0: A Truly Funny and Challenging Chinese Humor Understanding Dataset from Ruo Zhi Ba
Ruiqi He, Yushu He, Longju Bai +6
Existing humor datasets and evaluations predominantly focus on English, lacking resources for culturally nuanced humor in non-English languages like Chinese. To address this gap, w…
Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost
Oana Ignat, Longju Bai, Joan Nwatu +1
Current foundation models have shown impressive performance across various tasks. However, several studies have revealed that these models are not effective for everyone due to the…