On the Semantics of Large Language Models

• URL: http://arxiv.org/abs/2507.05448v1
• Date: Mon, 07 Jul 2025 20:02:57 GMT
• Title: On the Semantics of Large Language Models
• Authors: Martin Schuele,
• Abstract summary: Large Language Models (LLMs) demonstrated the potential to replicate human language abilities through technology.<n>It remains controversial to what extent these systems truly understand language.<n>We examine this issue by narrowing the question down to the semantics of LLMs at the word and sentence level.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) such as ChatGPT demonstrated the potential to replicate human language abilities through technology, ranging from text generation to engaging in conversations. However, it remains controversial to what extent these systems truly understand language. We examine this issue by narrowing the question down to the semantics of LLMs at the word and sentence level. By examining the inner workings of LLMs and their generated representation of language and by drawing on classical semantic theories by Frege and Russell, we get a more nuanced picture of the potential semantic capabilities of LLMs.

Related papers

• Language Surgery in Multilingual Large Language Models [32.77326546076424]
  Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across tasks and languages.<n>This paper investigates the naturally emerging representation alignment in LLMs, particularly in the middle layers.<n>We propose Inference-Time Language Control (ITLC) to enable precise cross-lingual language control and mitigate language confusion.
  arXiv  Detail & Related papers  (2025-06-14T11:09:50Z)
• The Emergence of Abstract Thought in Large Language Models Beyond Any Language [95.50197866832772]
  Large language models (LLMs) function effectively across a diverse range of languages.<n>Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts.<n>Recent results show strong multilingual performance, even surpassing English performance on specific tasks in other languages.
  arXiv  Detail & Related papers  (2025-06-11T16:00:54Z)
• XToM: Exploring the Multilingual Theory of Mind for Large Language Models [57.9821865189077]
  Existing evaluations of Theory of Mind in LLMs are largely limited to English.<n>We present XToM, a rigorously validated multilingual benchmark that evaluates ToM across five languages.<n>Our findings expose limitations in LLMs' ability to replicate human-like mentalizing across linguistic contexts.
  arXiv  Detail & Related papers  (2025-06-03T05:23:25Z)
• Sparse Auto-Encoder Interprets Linguistic Features in Large Language Models [40.12943080113246]
  We present a systematic and comprehensive causal investigation using sparse auto-encoders (SAEs)<n>We extract a wide range of linguistic features from six dimensions.<n>We introduce two indices-Feature Representation Confidence (FRC) and Feature Intervention Confidence (FIC)-to measure the ability of linguistic features to capture and control linguistic phenomena.
  arXiv  Detail & Related papers  (2025-02-27T18:16:47Z)
• Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis [5.029635172046762]
  Language Confusion is a phenomenon where Large Language Models (LLMs) generate text that is neither in the desired language, nor in a contextually appropriate language.<n>We introduce a novel metric, Language Confusion Entropy, designed to measure and quantify this confusion.
  arXiv  Detail & Related papers  (2024-10-17T05:43:30Z)
• Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models [11.423589362950812]
  Large language models (LLMs) have demonstrated remarkable performance, particularly in multilingual contexts.<n>Recent studies suggest that LLMs can transfer skills learned in one language to others, but the internal mechanisms behind this ability remain unclear.<n>This paper provides insights into the internal workings of LLMs, offering a foundation for future improvements in their cross-lingual capabilities.
  arXiv  Detail & Related papers  (2024-10-15T15:49:15Z)
• Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
  Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs. Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
  arXiv  Detail & Related papers  (2024-02-26T09:36:05Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering [52.86931192259096]
  Knowledge Base Question Answering (KBQA) aims to answer natural language questions based on facts in knowledge bases. Recent works leverage the capabilities of large language models (LLMs) for logical form generation to improve performance.
  arXiv  Detail & Related papers  (2024-01-11T09:27:50Z)
• Let Models Speak Ciphers: Multiagent Debate through Embeddings [84.20336971784495]
  We introduce CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. By deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs.
  arXiv  Detail & Related papers  (2023-10-10T03:06:38Z)
• Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners [75.85554779782048]
  Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned textitsemantics of language tokens do the most heavy lifting during the reasoning process.
  arXiv  Detail & Related papers  (2023-05-24T07:33:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.