Papers with leaderboard
ExplainaBoard: An Explainable Leaderboard for NLP (2021.acl-demo)
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Pengfei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaichen Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, Graham Neubig
| Challenge: | Using leaderboards, researchers can track the performance of various systems on various NLP tasks. |
| Approach: | They propose a new conceptualization and implementation of NLP evaluation using a leaderboard. |
| Outcome: | The ExplainaBoard is an evaluation tool for natural language processing (NLP) it covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks. |
Text-based NP Enrichment (2022.tacl-1)
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| Challenge: | Existing NLP tasks and benchmarks do not cover all NP-mediated relations . we aim to enrich each NP in a text with all the preposition-mediated relationships that hold between it and other NPs in the text. |
| Approach: | They propose a task to enrich NPs with preposition-mediated relations that hold between them . they build a large-scale dataset and analyze the data to test the task . |
| Outcome: | The proposed task is based on a large-scale dataset and fine-tuned language models. |
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions (2020.emnlp-main)
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| Challenge: | Existing reading comprehension tasks focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system’s performance at identifying a potential lack of sufficient information and locating sources for that information. |
| Approach: | They propose to use a dataset with 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents. |
| Outcome: | The proposed model achieves 31.1% F1 on the reading comprehension task, while estimated human performance is 88.4%. |
Evidence-based Fact-Checking of Health-related Claims (2021.findings-emnlp)
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| Challenge: | Existing evidence-based factchecking datasets contain synthetic claims and lack real-world verification. |
| Approach: | They propose a dataset for evidence-based fact-checking of health-related claims that evaluates their truthfulness against scientific articles. |
| Outcome: | The proposed dataset evaluates real-world claims against scientific articles. |
Visuo-Linguistic Question Answering (VLQA) Challenge (2020.findings-emnlp)
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| Challenge: | Understanding images and text together is an important aspect of cognition and building advanced AI systems. |
| Approach: | They propose to derive joint inference about a given image-text modality and compile a question-answering corpus using an image and a reading passage. |
| Outcome: | The proposed method has better baseline performance but is still far behind human performance. |
Efficient Performance Tracking: Leveraging Large Language Models for Automated Construction of Scientific Leaderboards (2024.emnlp-main)
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| Challenge: | Existing leaderboards are incomplete and some contain incorrect information. |
| Approach: | They propose a manually-curated Scientific Leaderboard dataset that overcomes these problems . they propose three experimental settings where TDM triples are fully defined, partially defined, or undefined . |
| Outcome: | The proposed system overcomes the shortcomings of existing leaderboard datasets . it can be used to evaluate and compare scientific methods, but it requires manual labor . |
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models (2022.emnlp-main)
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| Challenge: | a comprehensive evaluation of QM models should be conducted on natural texts, not on artificial adversarial examples . ral models are often not robust to adversarials, which means they predict unexpected outputs . |
| Approach: | They use a Chinese dataset to evaluate the robustness of QM models . they show that the effect of artificial adversarial examples does not work on natural texts . |
| Outcome: | The proposed model is more robust than other models on natural questions with 32 linguistic perturbations. |
MERA: A Comprehensive LLM Evaluation in Russian (2024.acl-long)
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Alena Fenogenova, Artem Chervyakov, Nikita Martynov, Anastasia Kozlova, Maria Tikhonova, Albina Akhmetgareeva, Anton Emelyanov, Denis Shevelev, Pavel Lebedev, Leonid Sinev, Ulyana Isaeva, Katerina Kolomeytseva, Daniil Moskovskiy, Elizaveta Goncharova, Nikita Savushkin, Polina Mikhailova, Anastasia Minaeva, Denis Dimitrov, Alexander Panchenko, Sergey Markov
| Challenge: | Recent advances in foundation models have led to the emergence of powerful Large Language Models (LLMs), which showcase unprecedented tasksolving capabilities. |
| Approach: | They propose a method to evaluate FMs and LMs in fixed zero- and few-shot instruction settings that can be extended to other modalities. |
| Outcome: | The proposed evaluation methodology includes an open-source code base and a leaderboard with a submission system. |
Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition (2025.findings-acl)
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| Challenge: | Existing research has evaluated large language models' cultural knowledge and contextual understanding, reducing their effectiveness in multicultural settings. |
| Approach: | They propose a benchmark to evaluate LLMs' cultural understanding with a focus on Korean superstitions. |
| Outcome: | The proposed benchmark assesses multilingual LLMs in Korean and English to analyze their ability to reason about Korean cultural contexts and how language variations affect performance. |
TounsiBench: Benchmarking Large Language Models for Tunisian Arabic (2025.emnlp-main)
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| Challenge: | a dataset of Tunisian Arabic instructions and prompts is used to evaluate LLMs' ability to understand and generate responses in Tunisia . we assess the quality, correctness, relevance, and dialectal adherence of LLM responses . |
| Approach: | They propose a benchmark for evaluating the capabilities of large language models in Tunisian Arabic . they use a dataset of Tunisia Arabic instructions and prompts to evaluate their models . |
| Outcome: | The proposed model can judge quality, correctness, relevance, and dialectal adherence . the model can also generate a leaderboard for the Tunisian Arabic language . |