Papers with Google Search
Open Domain Question Answering with Conflicting Contexts (2025.findings-naacl)
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Siyi Liu, Qiang Ning, Kishaloy Halder, Zheng Qi, Wei Xiao, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth
| Challenge: | Open domain question answering systems often rely on information retrieved from large collections of text to answer questions. |
| Approach: | They evaluate and benchmark three powerful Large Language Models with a dataset . they find that 25% of unambiguous open domain questions can lead to conflicting contexts . |
| Outcome: | The proposed model can't be used to answer questions with conflicting contexts . it can be fine tuned to provide richer information into the model's training . |
SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback (2026.findings-eacl)
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Fangyuan Xu, Rujun Han, Yanfei Chen, Zifeng Wang, I-Hung Hsu, Jun Yan, Vishy Tirumalashetty, Eunsol Choi, Tomas Pfister, Chen-Yu Lee
| Challenge: | High-quality, complex question-answer pairs are pivotal for training and evaluating capable deep search agents. |
| Approach: | They propose a pipeline that generates high-quality, difficulty-controlled deep search question-answer pairs for a given corpus and a target difficulty level. |
| Outcome: | The proposed pipeline generates high-quality, difficulty-controlled deep search question-answer pairs for a given corpus and a target difficulty level. |
VeriFastScore: Speeding up long-form factuality evaluation (2025.findings-emnlp)
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| Challenge: | VeriFastScore model can be used to evaluate long-form factuality but requires multiple LLM calls and can take up to 100s to evaluate a single response. |
| Approach: | They propose a model that leverages synthetic data to fine-tune Llama3.1 8B for extracting and verifying all verifiable claims within a given text based on evidence from Google Search. |
| Outcome: | The proposed model achieves strong correlation with the original VeriScore pipeline at both the example level and system level while achieving an overall speedup of 6.6 over Veriscore. |
LitSearch: A Retrieval Benchmark for Scientific Literature Search (2024.emnlp-main)
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| Challenge: | Literature search questions pose significant challenges for modern retrieval systems . a lack of domain expertise and reasoning through lengthy papers is a challenge . |
| Approach: | They propose a retrieval benchmark for literature search queries using inline citations from papers and questions about recently published papers. |
| Outcome: | The proposed retrieval benchmarks outperform state-of-the-art retrieval models and reranking pipelines. |