Papers with realistic setting
Realistic Citation Count Prediction Task for Newly Published Papers (2023.findings-eacl)
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| Challenge: | Existing studies on citation count prediction assume that future citation counts of academic papers have not had enough time pass since publication. |
| Approach: | They propose to use citation counts of newly published papers as a realistic citation count prediction task and to use them to leverage the citations of papers shortly after publication. |
| Outcome: | The proposed methods significantly improve the performance of citation count prediction for newly published papers in a realistic setting. |
From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation (2025.coling-main)
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| Challenge: | Existing data for low-resource languages are limited; the languages that could most benefit from domain adaptation (DA) are the ones left behind. |
| Approach: | They propose a realistic setting in which they aim to translate between a high-resource and a low-resourced language with limited parallel data, a bilingual dictionary, and c) a monolingual target-domain corpus in the high-rsource language. |
| Outcome: | The proposed methods are compared with a human evaluation of DALI and show that the most effective is the simplest. |
Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario (2022.emnlp-main)
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| Challenge: | Existing models fail to learn and use culinary knowledge in a compositional way, argues a new study. |
| Approach: | They propose a task that asks models to modify a base recipe according to the change of an ingredient. |
| Outcome: | The proposed model can perform compositional generalization in a realistic setting . existing models have difficulties in modifying ingredients while preserving original style . |
A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing (2023.emnlp-main)
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| Challenge: | Existing frameworks for dialogic discourse parsing are not suitable for contentious discussions . authors propose a model for non-convergent discourse paring that does not require label collocation . |
| Approach: | They propose a multi-label scheme for contentious dialog parsing that uses multiple labels . they propose combining embeddings of the utterance, context and the labels through GRN layers . |
| Outcome: | The proposed model achieves comparable results with SOTA without label collocation and without training a unique architecture/model for each label. |
From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions (2025.acl-long)
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Nathanaël Carraz Rakotonirina, Mohammed Hamdy, Jon Ander Campos, Lucas Weber, Alberto Testoni, Marzieh Fadaee, Sandro Pezzelle, Marco Del Tredici
| Challenge: | Large Language Models excel at solving individual problems in isolation, but are they able to effectively collaborate over long-term interactions? |
| Approach: | They propose to use a multi-session dataset to test LLMs' ability to track and execute simple coding instructions amid irrelevant information, simulating a realistic setting. |
| Outcome: | The proposed model performs poorly when instructions are spread across sessions, suggesting that they are not able to integrate information over long interactions. |
RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking (2026.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) integrates knowledge from tables with an external knowledge base to improve the answer relevance and accuracy. |
| Approach: | They propose a table-corpora-aware RAG framework called T-RAG to integrate external knowledge into Large Language Models (LLMs) they then develop a multi-table question answering benchmark called MultiTableQA which spans 3 different task types, 57,193 tables, and 23,758 questions in total. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy, recall, and runtime performance, with improvements of up to 9.4%. |