Benjamin V. Rozonoyer

2026

1. Preprint

   Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders

   Benjamin Rozonoyer, Chong You, Michael Boratko, and 5 more authors

   arXiv preprint arXiv:2601.05588, 2026

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   Dual and cross encoders have long been mainstays of information retrieval (IR), but are being challenged by the emergent capabilities of LLMs. An LLM-based approach we term pointwise generative ranking - generating tokens the length of a single docID as opposed to a list in order to enable ranking via beam search - combines efficiency and expressivity benefits while leveraging the in-context capabilities of Causal Transformers. Although there is ample evidence to suggest that pretrained LLMs are well-suited for ranking, we find that the vast majority of LLM-based approaches rely on next-token prediction, a loss function which is fundamentally rank-agnostic (and especially so with pointwise supervision). In this paper, we first prove that the expressivity of pointwise generative ranking with multi-token docIDs is superior to that of dual encoders. We then propose SToICaL - a Simple Token-Item Calibrated Loss - which can incorporate rank-aware supervision at both the item and token levels within the pointwise setup. We run a suite of experiments on ranking tasks derived from WordNet (Fellbaum, 1998) and ESCI (Reddy et al., arXiv:2206.06588). Two variants of SToICaL successfully suppress the probability of invalid docID generations and improve on common ranking metrics beyond top-1 retrieval.

   @article{rozonoyer2026autoregressive,
     author = {Rozonoyer, Benjamin and You, Chong and Boratko, Michael and Jain, Himanshu and Gupta, Nilesh and Bhojanapalli, Srinadh and McCallum, Andrew and Yu, Felix},
     journal = {arXiv preprint arXiv:2601.05588},
     year = {2026},
   }

2024

1. NeurIPS

   Learning Representations for Hierarchies with Minimal Support

   Benjamin Rozonoyer, Michael Boratko, Dhruvesh Patel, and 4 more authors

   In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024

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   When training node embedding models to represent large directed graphs (digraphs), it is impossible to observe all entries of the adjacency matrix during training. As a consequence most methods employ sampling. For very large digraphs, however, this means many (most) entries may be unobserved during training. In general, observing every entry would be necessary to uniquely identify a graph, however if we know the graph has a certain property some entries can be omitted - for example, only half the entries would be required for a symmetric graph. In this work, we develop a novel framework to identify a subset of entries required to uniquely distinguish a graph among all transitively-closed DAGs. We give an explicit algorithm to compute the provably minimal set of entries, and demonstrate empirically that one can train node embedding models with greater efficiency and performance, provided the energy function has an appropriate inductive bias. We achieve robust performance on synthetic hierarchies and a larger real-world taxonomy, observing improved convergence rates in a resource-constrained setting while reducing the set of training examples by as much as 99%.

   @inproceedings{rozonoyerlearning,
     title = {Learning Representations for Hierarchies with Minimal Support},
     author = {Rozonoyer, Benjamin and Boratko, Michael and Patel, Dhruvesh and Zhao, Wenlong and Dasgupta, Shib Sankar and Le, Hung and McCallum, Andrew},
     booktitle = {The Thirty-eighth Annual Conference on Neural Information Processing Systems},
     year = {2024},
   }

2021

1. EMNLP

   ExcavatorCovid: Extracting events and relations from text corpora for temporal and causal analysis for COVID-19

   Bonan Min, Benjamin Rozonoyer, Haoling Qiu, and 2 more authors

   arXiv preprint arXiv:2105.01819, 2021

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   Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are reported at such a speed and scale as to be overwhelming. In this paper, we present ExcavatorCovid, a machine reading system that ingests open-source text documents (e.g., news and scientific publications), extracts COVID-19 related events and relations between them, and builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help government agencies alleviate the information overload, understand likely downstream effects of political and economic decisions and events related to the pandemic, and respond in a timely manner to mitigate the impact of COVID-19. We expect the utility of Excavator to outlive the COVID-19 pandemic: analysts and decision makers will be empowered by Excavator to better understand and solve complex problems in the future. A demonstration video is available at https://vimeo.com/528619007.

   @article{min2021excavatorcovid,
     title = {ExcavatorCovid: Extracting events and relations from text corpora for temporal and causal analysis for COVID-19},
     author = {Min, Bonan and Rozonoyer, Benjamin and Qiu, Haoling and Zamanian, Alexander and MacBride, Jessica},
     journal = {arXiv preprint arXiv:2105.01819},
     year = {2021},
   }