Papers with LA
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models (2025.findings-acl)
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Boyang Xue, Hongru Wang, Rui Wang, Sheng Wang, Zezhong Wang, Yiming Du, Bin Liang, Wenxuan Zhang, Kam-Fai Wong
| Challenge: | Existing studies on LLM confidence estimations in languages other than English have been limited to English. |
| Approach: | They propose to use question-related language to prompt LLMs to assess their confidence in large language models. |
| Outcome: | The proposed model improves on question-related language prompts for LS tasks, while English exhibits notable linguistic dominance in confidence estimations. |
IndoCL: Benchmarking Indonesian Language Development Assessment (2024.findings-emnlp)
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| Challenge: | Recent interest has surged in applying natural language processing (NLP) and machine learning (ML) to evaluate language development in both first (L1) and second (L2) language acquisition. |
| Approach: | They propose to use an Indonesian corpus as a benchmark for LDA tasks and to use existing large-scale language models to improve performance. |
| Outcome: | The proposed model extracts language-independent features, relieving laborious computation and reliance on specific language. |
ClozEx: A Task toward Generation of English Cloze Explanation (2023.findings-emnlp)
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| Challenge: | Existing tasks and datasets specifically designed for generating language learner explanations for cloze questions are lacking . clozing questions are used to assess language proficiency and enhance language learning . |
| Approach: | They propose a task ClozEx to generate explanations for cloze questions in LA . they use a curated dataset of clozing questions paired with explanations . |
| Outcome: | The proposed task generates fluent explanations for cloze questions in English as a second language learners. |
RuCoLA: Russian Corpus of Linguistic Acceptability (2022.emnlp-main)
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Vladislav Mikhailov, Tatiana Shamardina, Max Ryabinin, Alena Pestova, Ivan Smurov, Ekaterina Artemova
| Challenge: | Recent research has focused on evaluating the grammatical knowledge of language models with acceptability judgments. |
| Approach: | They propose to build a corpus of linguistic acceptability in Russian using a binary LA approach. |
| Outcome: | The proposed set of tests shows that the most widely used language models still fall behind humans by a large margin when detecting morphological and semantic errors. |
Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model (2025.acl-long)
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Emre Can Acikgoz, Jeremiah Greer, Akul Datta, Ze Yang, William Zeng, Oussama Elachqar, Emmanouil Koukoumidis, Dilek Hakkani-Tür, Gokhan Tur
| Challenge: | Large Language Models (LLMs) with API-calling capabilities enabled building effective Language Agents (LA) current approaches excel in one domain but underperform in the other. |
| Approach: | They propose a unified approach that integrates both conversational and agentic capabilities. |
| Outcome: | The proposed model outperforms top domain-specific models across three benchmarks. |