Investigating the (De)Composition Capabilities of Large Language Models in Natural-to-Formal Language Conversion (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing frameworks for evaluating the decomposition and composition capabilities of large language models (LLMs) in N2F are inadequate, and there are errors that can be attributed to deficiencies in natural language understanding and the learning and use of symbolic systems. |
| Approach: | They propose a framework that semi-automatically performs sample and task construction . main findings include that LLMs are deficient in both decomposition and composition . |
| Outcome: | The proposed framework evaluates the most advanced LLMs on a variety of common formal languages. |
Similar Papers
FAC2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are evaluated by overall performance on various text understanding and generation tasks. |
| Approach: | They propose a framework for Fine-grAined and Cognition-grounded LLMs’ Capability Evaluation that dissociates the language-related capabilities from cognition-related ones. |
| Outcome: | The proposed framework dissociates the language-related capabilities from cognition-related ones and breaks down the process of applying a specific capability into three sub-steps: recalling relevant knowledge, utilizing knowledge, and solving problems. |
Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)
Copied to clipboard
Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠 林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang
| Challenge: | Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications. |
| Approach: | They propose a reliable strategy for domains to choose more robust LLMs for real-world applications. |
| Outcome: | The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications. |
Assessing the Capabilities of Large Language Models in Coreference: An Evaluation (2024.lrec-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are a new approach to coreference resolution, but their performance is not yet fully understood. |
| Approach: | They propose that future efforts should improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. |
| Outcome: | The proposed methods improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. |
Evaluating the Deductive Competence of Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing large language models have limited abilities to solve deductive reasoning problems . performance differences between conditions do not improve overall performance . |
| Approach: | They investigate whether several large language models can solve a deductive reasoning problem in their conventional form. |
| Outcome: | The proposed models can solve a classic type of deductive reasoning problem in their conventional form. |
From A and B to A+B: Can Large Language Models Solve Compositional Math Problems? (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies that create problem variants by adding perturbations to a single problem focus on the interaction between problems. |
| Approach: | They propose a pipeline with 98.2% accuracy to combine two original problems with a logical connection and to evaluate LLMs' generalization ability on the compositional problems. |
| Outcome: | The proposed pipeline can combine two original problems with a logical connection to get a new math problem and evaluate its compositional generalization on the compositional problems. |
How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have validated that large language models (LLMs) are capable of solving some KBQA problems, but there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing. |
| Approach: | They propose to evaluate the understanding and generation ability of large language models (LLMs) to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs. |
| Outcome: | The proposed model can understand formal languages as well as humans, but generating correct logical forms remains a challenge. |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
| Outcome: | The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses. |
Exploring Compositional Generalization of Large Language Models (2024.naacl-srw)
Copied to clipboard
| Challenge: | a recent study has found that large language models can generalize compositional instructions from simple instructions to complex ones. |
| Approach: | They study the generalization ability of large language models with respect to compositional instructions . they first construct a dataset with the help of ChatGPT guided by the self-instruct technique . |
| Outcome: | The proposed model can generalize from simple instructions to more intricate ones, the authors show . their results show that training LLMs on higher-order compositional instructions improves performance on lower-order ones, but not on higher order ones. |
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language. |
| Approach: | They propose a model that integrates symbolic data into LLM training without loss of generality ability. |
| Outcome: | The proposed model performs better on symbol- and NL-centric tasks. |
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
Copied to clipboard
Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
| Challenge: | Large Language Models (LLMs) have gained significant attention due to their capabilities in performing diverse tasks across domains. |
| Approach: | They review the primary challenges and limitations causing inconsistencies in evaluations . early models could generate coherent text but limited to simple tasks . |
| Outcome: | The proposed evaluations are reproducible, reliable, and robust. |