Challenge: Symbols are used in abstract reasoning, chemical property prediction, and tabular question-answering.
Approach: They propose a method that converts symbols to language-based representations to improve their accuracy.
Outcome: The proposed method improves the accuracy of symbols in language-based models.

Similar Papers

Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)

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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.
Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models (2024.findings-acl)

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Challenge: Existing methods rely on syntactically mapping natural languages to complete formal languages like Python and SQL.
Approach: They propose to deconstruct reasoning-independent semantic information into generic symbolic representations, thereby efficiently capturing more generalized reasoning knowledge.
Outcome: The proposed method improves in-context reasoning accuracy, learning efficiency, out-of-domain generalization, and output stability compared to the Chain-of thought technique.
STEM-POM: Evaluating Language Models Math-Symbol Reasoning in Document Parsing (2025.findings-acl)

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Challenge: Advances in large language models have spurred research into enhancing their reasoning capabilities, particularly in math-rich STEM documents.
Approach: They propose a benchmark dataset to evaluate LLMs’ reasoning abilities on math symbols within contextual scientific text.
Outcome: The proposed dataset demonstrates that state-of-the-art LLMs achieve an average accuracy of 20-60% under in-context learning and 50-60% with fine-tuning, highlighting a substantial gap in their ability to classify mathematical symbols.
MTLS: Making Texts into Linguistic Symbols (2024.emnlp-main)

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Challenge: In linguistics, all languages can be considered as symbolic systems . most work overlooks the properties of languages as symbol systems - aaron et al., 1989).
Approach: They propose a method to make texts into linguistic symbols to improve multilingual capability . they use a pre-training method to replace pre-trained language models with a vocabulary map .
Outcome: The proposed method improves multilingual capabilities on multilingual tasks using BERT and RoBERTa as the backbone.
Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

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Challenge: specialized LLMs are often limited in domain-specific applications that require specialized knowledge.
Approach: They provide a comprehensive overview of four key methods to enhance large language models by integrating domain-specific knowledge.
Outcome: The proposed methods are categorized into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization.
LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder (2025.emnlp-main)

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Challenge: Prior research on linguistic mechanisms of large language models is limited by coarse granularity, limited analysis scale, and narrow focus.
Approach: They propose a framework for analyzing the linguistic mechanisms of large language models based on Sparse Auto-Encoders.
Outcome: The proposed framework extracts Chinese and English linguistic features across four dimensions . it uncovers intrinsic representations of linguistic knowledge in LLMs and can control outputs .
From A and B to A+B: Can Large Language Models Solve Compositional Math Problems? (2025.emnlp-main)

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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.
Large Language Models Can Learn Representation in Natural Language (2024.findings-acl)

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Challenge: Large Language Models (LLMs) are unable to complete complex tasks involving multiple entities, such as tool APIs.
Approach: They propose a method which uses natural language representations to refine entity descriptions for improved retrieval and LLM utilization.
Outcome: The proposed method improves GPT-4's performance on classification tasks and API call tasks.
How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)

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Challenge: Entity alignment (EA) is critical for knowledge graph (KG) integration.
Approach: They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment.
Outcome: The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment.
Investigating the (De)Composition Capabilities of Large Language Models in Natural-to-Formal Language Conversion (2025.naacl-long)

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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.

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