Papers by Shayne Longpre
Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models (2024.emnlp-main)
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Seungone Kim, Juyoung Suk, Shayne Longpre, Bill Yuchen Lin, Jamin Shin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | Existing open-source evaluation paradigms lack flexibility and performance . language model-based evaluation is cheap and scalable, but it is difficult to evaluate . |
| Approach: | They propose a language model-based evaluation paradigm that uses a scalar indicator of quality to assess LM outputs. |
| Outcome: | The proposed language model-based evaluation model is more powerful than its predecessor. |
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2021.tacl-1)
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| Challenge: | Existing multilingual QA datasets lack linguistic diversity and comparable evaluation between languages. |
| Approach: | They propose a multilingual question-answer evaluation set with 10k English queries and human translations of them into 25 additional languages and dialects. |
| Outcome: | The proposed model is based on a multilingual knowledge questions and answers evaluation set with 26 languages. |
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks (2022.coling-1)
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| Challenge: | Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. |
| Approach: | They compare accuracy vs. model size tradeoffs using quantization and distillation methods . they find that pruning provides greater benefit than quantization . |
| Outcome: | The proposed methods reduce model size and can accelerate inference, but their relative benefit and combinatorial interactions have not been rigorously studied. |
Open-Domain Question Answering Goes Conversational via Question Rewriting (2021.naacl-main)
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Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi
| Challenge: | Existing large-scale benchmarks for conversational QA limit the topic of conversation to the content of a single document. |
| Approach: | They propose a dataset for Question Rewriting in Conversational Context (QReCC) the dataset contains 14K conversations with 80K question-answer pairs. |
| Outcome: | The proposed approach shows that the first baseline for the QReCC dataset is 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement. |
Entity-Based Knowledge Conflicts in Question Answering (2021.emnlp-main)
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| Challenge: | Knowledge-dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a passage at inference time. |
| Approach: | They propose a method to mitigate over-reliance on parametric knowledge, which minimizes hallucination, and improves out-of-distribution generalization by 4% - 7%. |
| Outcome: | The proposed method minimizes hallucination and improves generalization to evolving information by 4% - 7%. |
On the Transferability of Minimal Prediction Preserving Inputs in Question Answering (2021.naacl-main)
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| Challenge: | Recent work establishes the presence of short, uninterpretable input fragments that yield high confidence and accuracy in neural models. |
| Approach: | They investigate competing hypotheses for the existence of MPPIs in question answering . they discover a perplexing invariance of MPIs to random training seed, model architecture, pretraining, and training domain. |
| Outcome: | The proposed model performance is higher than comparable short queries. |
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering (D19-58)
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| Challenge: | XLNet model is domain-agnostic for the MRQA 2019 Shared Task . a negative sampling technique is particularly effective for datasets that include unanswerable questions . |
| Approach: | They develop a domain-agnostic question answering model for the MRQA 2019 Shared Task . they use large pre-trained language models, various data sampling strategies and query and context paraphrases generated by back-translation . |
| Outcome: | The proposed model achieves second best Exact Match and F1 in the MRQA leaderboard competition. |
Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP (2021.acl-long)
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| Challenge: | Existing studies show that retrievers underperform on rarer entities that share a name . open-domain tasks require a knowledge source to perform reasoning and produce an answer . |
| Approach: | They propose an evaluation benchmark for retrieving entities that share a name . they define Ambiguous Entity Retrieval sets as a collection of entities that have a common name - and query about those entities. |
| Outcome: | The proposed sets underperform on rarer entities that share a name . the retrievers exhibit popularity bias, and are twice as likely to retrieve erroneous documents . |
A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity (2024.naacl-long)
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Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Wei, Kevin Robinson, David Mimno, Daphne Ippolito
| Challenge: | a large number of pretraining data design practices are under-documented, authors say . authors: strong performance of modern language models depends on selfsupervised pretraining . |
| Approach: | They propose to pretrain models on data curated at different collection times . they find temporal shift between evaluation data and pretraining data leads to performance degradation . |
| Outcome: | The results validate, quantify, and expose many undocumented intuitions about text pretraining . authors say this practice has outperformed other models in the field . |
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? (2020.findings-emnlp)
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| Challenge: | Task-agnostic data augmentations have proven widely effective in computer vision, even on pretrained models. |
| Approach: | They examine the effects of two types of task-agnostic data augmentation on pretrained transformers using 5 classification tasks and 6 datasets. |
| Outcome: | The proposed techniques improve performance on 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers. |
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)
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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment. |
| Approach: | They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation . |
| Outcome: | The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks. |
Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation (2025.acl-long)
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Shivalika Singh, Angelika Romanou, Clémentine Fourrier, David Ifeoluwa Adelani, Jian Gang Ngui, Daniel Vila-Suero, Peerat Limkonchotiwat, Kelly Marchisio, Wei Qi Leong, Yosephine Susanto, Raymond Ng, Shayne Longpre, Sebastian Ruder, Wei-Yin Ko, Antoine Bosselut, Alice Oh, Andre Martins, Leshem Choshen, Daphne Ippolito, Enzo Ferrante, Marzieh Fadaee, Beyza Ermis, Sara Hooker
| Challenge: | Reliable multilingual evaluation is difficult and culturally appropriate evaluation is even harder to achieve. |
| Approach: | They propose a multilingual evaluation framework that aims to mitigate these biases by improving translations and annotation practices. |
| Outcome: | The proposed framework improves translation quality and cultural coverage and is culturally sensitive and culturally agnostic. |