Papers by Corby Rosset
LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts (2024.acl-long)
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| Challenge: | Existing frameworks for the automated evaluation of natural language texts are based on a large language model (LLM) that fails to agree with human judges and is not fully validated by the human judges. |
| Approach: | They propose a large language model (LLM) that generates a distribution over potential responses to assess multiple dimensions of interest. |
| Outcome: | The proposed framework predicts human judges' assessment of user satisfaction on a scale of 1–4 with an RMS error 0.5, a 2 improvement over the uncalibrated baseline. |
Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents (2025.findings-acl)
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Vardaan Pahuja, Yadong Lu, Corby Rosset, Boyu Gou, Arindam Mitra, Spencer Whitehead, Yu Su, Ahmed Hassan Awadallah
| Challenge: | Recent success in large multimodal models (LMMs) has sparked promising applications of agents capable of autonomously completing complex web tasks. |
| Approach: | They propose a scalable recipe to synthesize the largest and most diverse trajectory-level dataset to date. |
| Outcome: | The proposed model synthesizes the largest and most diverse trajectory-level dataset to date, with 94K successful multimodal web trajectories, 720K screenshots, and 33M web elements. |
Augmenting Zero-Shot Dense Retrievers with Plug-in Mixture-of-Memories (2023.emnlp-main)
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| Challenge: | Using mixture-of-memory augmenting to augment language models improves model generalization but with diminishing return. |
| Approach: | They develop a mechanism that augments language models with mixture-of-memory Augmentation (MoMA) they augment strong T5-based retrievers with the option to "plug in" unseen memory at inference time. |
| Outcome: | The proposed model outperforms methods with larger model sizes on the BEIR benchmark and achieves comparable or even better performance than methods relying on target-specific pretraining. |
Axiomatic Preference Modeling for Longform Question Answering (2023.emnlp-main)
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| Challenge: | Recent advances in large language models have helped bridge the "alignment gap" between the responses of raw pretrained language models and responses that resonate more closely with human preferences. |
| Approach: | They propose to use a axiomatic framework to generate a rich variety of preference signals to uphold these signals. |
| Outcome: | The proposed model outperforms GPT-4 and ChatGPT in preference scoring. |
Automatic Pair Construction for Contrastive Post-training (2024.findings-naacl)
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Canwen Xu, Corby Rosset, Ethan Chau, Luciano Corro, Shweti Mahajan, Julian McAuley, Jennifer Neville, Ahmed Awadallah, Nikhil Rao
| Challenge: | Large language models (LLMs) have unprecedented proficiency in a wide array of tasks. |
| Approach: | They propose a way to construct contrastive data using preference pairs from multiple models of varying strengths using SLiC and DPO. |
| Outcome: | The proposed method outperforms existing models like Orca in the comparison of SLiC and DPO with SFT baselines. |
Dodo: Dynamic Contextual Compression for Decoder-only LMs (2024.acl-long)
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| Challenge: | Existing approaches to NLP are sparsifying attention patterns or approximating the attention computation with kernel methods. |
| Approach: | They propose a method for dynamic contextual compression for decoder-only LMs. |
| Outcome: | The proposed method reduces the cost of self-attention to a fraction of typical time and space. |