mlsanityData to 7 Oct 2026

Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints

arXiv 2409.14469 ↗First posted 22 Sep 2024cs.CLcited by 5

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Semantic Parsing aims to capture the meaning of a sentence and convert it into a logical, structured form. Previous studies show that semantic parsing enhances the performance of smaller models (e.g., BERT) on downstream tasks. However, it remains unclear whether the improvements extend similarly to LLMs. In this paper, our empirical findings reveal that, unlike smaller models, directly adding semantic parsing results into LLMs reduces their performance. To overcome this, we propose SENSE, a novel prompting approach that embeds semantic hints within the prompt. Experiments show that SENSE consistently improves LLMs' performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 11 of its 29 references that are in the map. First posted on arXiv 22 Sep 2024.

Builds on

11 of 29 references.

References · 11

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  3. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  4. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 628
  5. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 361
  6. 2023How Good Are GPT Models at Machine Translation? A Comprehensive Evaluationcited by 344
  7. 2023EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizerscited by 189
  8. 2023BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Modelscited by 42
  9. 2018Semantic Structural Evaluation for Text Simplificationcited by 30
  10. 2023Deliberate then Generate: Enhanced Prompting Framework for Text Generationcited by 13
  11. 2024Analyzing the Role of Semantic Representations in the Era of Large Language Modelscited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Large Language Models as Computable Approximations to Solomonoff Inductioncited by 6
  2. 2025SR-LLM: Rethinking the Structured Representation in Large Language Modelcited by 3
  3. 2025GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Modelscited by 3
  4. 2025Efficient Mixture-of-Agents Serving via Tree-Structured Routing, Adaptive Pruning, and Dependency-Aware Prefill-Decode Overlapcited by 0
  5. 2026CBRS: Cognitive Blood Request System with Bilingual Dataset and Dual-Layer Filtering for Multi-Platform Social Streamscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt
  2. 2025Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understanding
  3. 2024Traffic Light or Light Traffic? Investigating Phrasal Semantics in Large Language Models
  4. 2026Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models
  5. 2026Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models
  6. 2026SemBench: A Universal Semantic Framework for LLM Evaluation
  7. 2024Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models
  8. 2025Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs