6 citations · 9 across the 11 of their papers we have counts for
5 papers · 1 filter
DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research
Rulin Shao, Akari Asai, Shannon Zejiang Shen +18
Deep research agents perform multi-step research to produce long-form, well-attributed answers. However, most open deep research agents are trained on easily verifiable short-form…
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Song Wang, Zihan Chen, Peng Wang +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or special…
Train for Truth, Keep the Skills: Binary Retrieval-Augmented Reward Mitigates Hallucinations
Tong Chen, Akari Asai, Luke Zettlemoyer +2
Language models often generate factually incorrect information unsupported by their training data, a phenomenon known as extrinsic hallucination. Existing mitigation approaches oft…
Deep Research Comparator: A Platform For Fine-grained Human Annotations of Deep Research Agents
Prahaladh Chandrahasan, Jiahe Jin, Zhihan Zhang +9
Effectively evaluating deep research agents that autonomously search the web, analyze information, and generate reports remains a major challenge, particularly when it comes to ass…
Out of Style: RAG's Fragility to Linguistic Variation
Tianyu Cao, Neel Bhandari, Akhila Yerukola +2
Despite the impressive performance of Retrieval-augmented Generation (RAG) systems across various NLP benchmarks, their robustness in handling real-world user-LLM interaction queri…