mlsanityData to 7 Oct 2026

Clause-Internal or Clause-External? Testing Turkish Reflexive Binding in Adapted versus Chain of Thought Large Language Models

arXiv 2602.00380 ↗First posted 30 Jan 2026cs.CLcited by 3

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

This study evaluates whether state-of-the-art large language models capture the binding relations of Turkish reflexive pronouns. We construct a balanced evaluation set of 100 Turkish sentences that systematically pit local against non-local antecedents for the reflexives kendi and kendisi. We compare two contrasting systems: an OpenAI chain-of-thought model optimized for multi-step reasoning and Trendyol-LLM-7B-base-v0.1, a LLaMA 2 derived model extensively fine-tuned on Turkish data. Antecedent choice is assessed using a combined paradigm that integrates sentence-level perplexity with a forced-choice comparison between minimally differing continuations. Overall, Trendyol-LLM favors local bindings in approximately 70 percent of trials, exhibiting a robust locality bias consistent with a preference for structurally proximate antecedents. By contrast, the OpenAI model (o1 Mini) distributes its choices nearly evenly between local and long-distance readings, suggesting weaker or less consistent sensitivity to locality in this binding configuration. Taken together, these results reveal a marked contrast in binding behavior across the two systems and motivate closer analysis of how model architecture, training data, and inference-time reasoning strategies shape the representation of Turkish anaphoric dependencies.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 19 of its 78 references that are in the map. First posted on arXiv 30 Jan 2026.

Builds on

19 of 78 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023PaLM 2 Technical Report17 May
  2. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Models30 Nov

Other references · 17

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  4. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  5. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  6. 2018SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inferencecited by 333
  7. 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 288
  8. 2018Targeted Syntactic Evaluation of Language Modelscited by 239
  9. 2023Evaluating Large Language Models in Theory of Mind Taskscited by 217
  10. 2018Colorless green recurrent networks dream hierarchicallycited by 210
  11. 2018What do RNN Language Models Learn about Filler-Gap Dependencies?cited by 86
  12. 2024BadChain: Backdoor Chain-of-Thought Prompting for Large Language Modelscited by 74
5 more
  1. 2024Aligning Language Models to Explicitly Handle Ambiguitycited by 55
  2. 2018RNNs as psycholinguistic subjects: Syntactic state and grammatical dependencycited by 23
  3. 2025Potemkin Understanding in Large Language Modelscited by 14
  4. 2025Tokens with Meaning: A Hybrid Tokenization Approach for Turkishcited by 7
  5. 2025LLMs Show Surface-Form Brittleness Under Paraphrase Stress Testscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026From Lemmas to Dependencies: What Signals Drive Light Verbs Classification?cited by 1
  2. 2026Plausibility as Commonsense Reasoning: Humans Succeed, Large Language Models Do notcited by 1
  3. 2026Benchmarking Source-Sensitive Reasoning in Turkish: Humans and LLMs under Evidential Trust Manipulationcited by 1

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