| Challenge: | Large language models (LLMs) have achieved remarkable success across various natural language processing tasks, but they still face challenges in performing fundamental NLP tasks, such as syntactic parsing. |
| Approach: | They propose a method that leverages grammar rules from existing treebanks to guide LLMs in correcting previous errors. |
| Outcome: | The proposed method significantly improves performance on in-domain and cross-domain datasets. |
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Large Language Models Are No Longer Shallow Parsers (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have reshaped the field of natural language processing (NLP) however, fundamental NLP tasks that involve linguistic analysis still play essential roles in the field. |
| Approach: | They propose to use constituency parsing to improve performance of LLMs on deep syntactic parse trees to prompt LLM chunking, filter out low-quality chunks and add remaining chunks to prompts to instruct LLM for parser. |
| Outcome: | The proposed approach improves LLMs' performance on constituency parsing on English and Chinese benchmark datasets. |
LLMs cannot find reasoning errors, but can correct them given the error location (2024.findings-acl)
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| Challenge: | Recent attempts to self-correct logical or reasoning errors often cause correct answers to become incorrect, resulting in poor performance overall. |
| Approach: | They propose to use a backtracking setup to test the correction abilities of LLMs on their mistake-finding ability to find logical mistakes. |
| Outcome: | The proposed model improves on 5 reasoning tasks, showing that it can correct logical mistakes without ground truth labels or training data. |
GRAMMAR-LLM: Grammar-Constrained Natural Language Generation (2025.findings-acl)
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| Challenge: | Existing approaches to fine-tuning and prompting are insufficient to ensure compliance with predefined taxonomies, syntactic structures, or domain-specific rules. |
| Approach: | They propose a framework that integrates formal grammatical constraints into the decoding process to enforce syntactic correctness in linear time while maintaining expressiveness in grammar rule definition. |
| Outcome: | The proposed framework enforces syntactic correctness in linear time while maintaining expressiveness in grammar rule definition. |
Grammar-Constrained Decoding Makes Large Language Models Better Logical Parsers (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) have shown capabilities in various natural language processing tasks, yet struggle with logical reasoning. |
| Approach: | They propose to combine Large Language Models with symbolic reasoners to improve syntactic correctness and semantic accuracy in logical parsing tasks. |
| Outcome: | The proposed approach improves syntactic correctness and semantic accuracy in logical parsing tasks. |
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction (2025.acl-industry)
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Yinghui Li, Shang Qin, Jingheng Ye, Haojing Huang, Yangning Li, Shu-Yu Guo, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu
| Challenge: | Recent studies have shown that Large Language Models’ performance as correctors on Chinese Grammatical Error Correction (CGEC) remains unsatisfactory due to the challenging nature of the task. |
| Approach: | They propose a training framework EXAM that uses LLMs as explainers to enhance CGEC small models and a novel evaluation method SEE that utilizes LLM as evaluators to bring more reasonable evaluations. |
| Outcome: | The proposed methods improve the performance of LLMs on Chinese Grammatical Error Correction (CGEC) task. |
Editing Large Language Models: Problems, Methods, and Opportunities (2023.emnlp-main)
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Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in model editing for LLMs have created challenges and opportunities for the community. |
| Approach: | They propose to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
| Outcome: | The proposed method alters behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
Small Language Models Need Strong Verifiers to Self-Correct Reasoning (2024.findings-acl)
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Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, Lu Wang
| Challenge: | Existing studies show that large language models can self-correct their outputs by generating a critique and revising it based on the critique. |
| Approach: | They propose a pipeline that prompts small language models to collect self-correction data that supports the training of self-refinement abilities. |
| Outcome: | The proposed pipeline improves the self-correction abilities of two models on five datasets spanning math and commonsense reasoning. |
Self-Improvement in Multimodal Large Language Models: A Survey (2025.findings-emnlp)
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| Challenge: | Using data and data, self-improvement for Large Language Models has improved model capabilities without significantly increasing costs. |
| Approach: | This survey provides a comprehensive overview of self-improvement for Large Language Models . it includes commonly used evaluations and downstream applications . |
| Outcome: | The authors provide a comprehensive overview of self-improvement in Multimodal LLMs. |
Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |
Harnessing Large Language Models as Post-hoc Correctors (2024.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their effectiveness in a wide range of tasks, including machine translation and commonsense reasoning. |
| Approach: | They propose a training-free framework that can work as a post-hoc corrector to propose corrections for ML models. |
| Outcome: | The proposed framework improves the performance of a number of models by up to 39% on text analysis and the challenging molecular predictions. |