The unreasonable effectiveness of pattern matching

• URL: http://arxiv.org/abs/2601.11432v1
• Date: Fri, 16 Jan 2026 16:53:08 GMT
• Title: The unreasonable effectiveness of pattern matching
• Authors: Gary Lupyan, Blaise Agüera y Arcas,
• Abstract summary: Large language models can make sense of "Jabberwocky" language in which most or all content words have been randomly replaced by nonsense strings.<n>The ability of LLMs to recover meaning from structural patterns speaks to the unreasonable effectiveness of pattern-matching.
• Score: 1.0780189313017459
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We report on an astonishing ability of large language models (LLMs) to make sense of "Jabberwocky" language in which most or all content words have been randomly replaced by nonsense strings, e.g., translating "He dwushed a ghanc zawk" to "He dragged a spare chair". This result addresses ongoing controversies regarding how to best think of what LLMs are doing: are they a language mimic, a database, a blurry version of the Web? The ability of LLMs to recover meaning from structural patterns speaks to the unreasonable effectiveness of pattern-matching. Pattern-matching is not an alternative to "real" intelligence, but rather a key ingredient.

Related papers

• A Typologically Grounded Evaluation Framework for Word Order and Morphology Sensitivity in Multilingual Masked LMs [2.895343274331944]
  We evaluate mBERT and XLM-R on English, Chinese, German, Spanish, and Russian languages.<n>Full scrambling drives word-level reconstruction accuracy near zero in all languages.<n>Top-5 word accuracy shows the same pattern: under full scrambling, the gold word rarely appears among the five highest-ranked reconstructions.
  arXiv  Detail & Related papers  (2026-02-28T03:13:34Z)
• The Astonishing Ability of Large Language Models to Parse Jabberwockified Language [0.0]
  Large language models (LLMs) have an astonishing ability to recover meaning from severely degraded English texts.<n>We show that structural cues (e.g., morphosyntax, closed-class words) constrain lexical meaning to a much larger degree than imagined.
  arXiv  Detail & Related papers  (2026-02-27T11:23:45Z)
• STITCH: Simultaneous Thinking and Talking with Chunked Reasoning for Spoken Language Models [131.90117151306993]
  Spoken Language Models (SLMs) are designed to take speech inputs and produce spoken responses.<n>Current SLMs lack the ability to perform an internal, unspoken thinking process before responding.<n>We propose Stitch, a novel generation method that alternates between the generation of unspoken reasoning chunks and spoken response chunks.
  arXiv  Detail & Related papers  (2025-07-21T08:30:03Z)
• On the Thinking-Language Modeling Gap in Large Language Models [68.83670974539108]
  We show that there is a significant gap between the modeling of languages and thoughts.<n>We propose a new prompt technique termed Language-of-Thoughts (LoT) to demonstrate and alleviate this gap.
  arXiv  Detail & Related papers  (2025-05-19T09:31:52Z)
• Unnatural Languages Are Not Bugs but Features for LLMs [92.8332103170009]
  Large Language Models (LLMs) have been observed to process non-human-readable text sequences, such as jailbreak prompts.<n>We present a systematic investigation challenging this perception, demonstrating that unnatural languages contain latent features usable by models.
  arXiv  Detail & Related papers  (2025-03-02T12:10:17Z)
• Idiosyncrasies in Large Language Models [54.26923012617675]
  We unveil and study idiosyncrasies in Large Language Models (LLMs)<n>We find that fine-tuning text embedding models on LLM-generated texts yields excellent classification accuracy.<n>We leverage LLM as judges to generate detailed, open-ended descriptions of each model's idiosyncrasies.
  arXiv  Detail & Related papers  (2025-02-17T18:59:02Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts.<n>We find that Llama Instruct and Mistral models exhibit high degrees of language confusion.<n>We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• Active Use of Latent Constituency Representation in both Humans and Large Language Models [9.995581737621505]
  We show that a latent tree-structured constituency representation can emerge in both the human brain and large language models. Results demonstrate that a latent tree-structured constituency representation can emerge in both the human brain and LLMs.
  arXiv  Detail & Related papers  (2024-05-28T14:50:22Z)
• Evaluating Dialect Robustness of Language Models via Conversation Understanding [2.8514881296685113]
  We use English language (US English or Indian English) conversations between humans who play the word-guessing game of 'taboo'<n>We formulate two evaluative tasks: target word prediction (TWP) ($textiti.e.$, predict the masked target word in a conversation) and target word selection (TWS) ($textiti.e.$, select the most likely masked target word in a conversation)<n>We create two subsets: en-MV (where en-US is transformed to include dialectal information) and en-TR (where dialectal information is
  arXiv  Detail & Related papers  (2024-05-09T11:38:23Z)
• Large language models and linguistic intentionality [0.0]
  I will argue that we should instead consider whether language models meet the criteria given by our best metasemantic theories of linguistic content. I will argue that it is a mistake to think that the failure of LLMs to meet plausible conditions for mental intentionality thereby renders their outputs meaningless.
  arXiv  Detail & Related papers  (2024-04-15T08:37:26Z)
• APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning [73.3035118224719]
  We propose APOLLO, an adaptively pretrained language model that has improved logical reasoning abilities. APOLLO performs comparably on ReClor and outperforms baselines on LogiQA.
  arXiv  Detail & Related papers  (2022-12-19T07:40:02Z)
• Characterizing Verbatim Short-Term Memory in Neural Language Models [19.308884420859027]
  We tested whether language models could retrieve the exact words that occurred previously in a text. We found that the transformers retrieved both the identity and ordering of nouns from the first list. Their ability to index prior tokens was dependent on learned attention patterns.
  arXiv  Detail & Related papers  (2022-10-24T19:47:56Z)

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.