4arXiv cs.CL (Computation and Language)·
Instruction-tuned LLMs show stronger syntactic convergence toward human interlocutors than humans do
A new arXiv preprint measures syntactic convergence in 16 open-weight Llama and Gemma models (1B–70B, pretrained and instruction-tuned) by substituting model outputs into pre-existing human dialogues and measuring context-free grammar rule reuse. Every model showed greater CFG-rule overlap with the preceding human turn than a random baseline, and instruction-tuned models exceeded the syntactic convergence of the human responses they replaced. Instruction tuning increased local syntactic and semantic alignment with interlocutors, though it also raised baseline overlap with unrelated primes, suggesting a more globally accommodating output style rather than purely targeted priming.
Related guides (2)
Related events (8)
Tracing the Emergence of Human-Like Pragmatic Reasoning in LLMs Across Languages
Researchers conducted a population-matching experiment evaluating 25 LLMs on conditional inference tasks across four languages, comparing model behavior to matched human populations. The study finds that LLMs function as accurate semantic operators but systematically fail to capture pragmatic enrichments—context-sensitive inferences beyond literal logical meaning—that humans apply effortlessly. Model performance on pragmatic reasoning is not predicted by open vs. closed weights, training orientation, or architecture type, suggesting pragmatic reasoning remains an emergent and unreliable capability. The findings contribute to ongoing debates about whether LLMs reason like humans or merely approximate surface-level linguistic patterns.
Synthetic LLM-generated conversations improve ASR training for low-resource languages
Researchers propose a pipeline that uses LLMs to generate scenario-level dialogues and TTS to synthesize multi-speaker audio, creating simulated conversational training data for ASR systems. Evaluated on the Hungarian BEA-Dialogue benchmark, a model trained on 67 hours of real plus 636 hours of synthetic data outperforms a zero-shot model trained on 2,700 hours of real Hungarian speech. The study tests five LLM families under multiple budget and mixing configurations using a FastConformer-Large backbone, finding that generator choice and data composition significantly affect gains.
Systematic study reveals effectiveness-fluency trade-offs in LLM conditioning methods
A new arXiv paper systematically evaluates a range of LLM conditioning methods across both concept injection and removal scenarios, finding that efficient steering methods often degrade fluency significantly. A key finding is that activation steering is substantially less effective on instruction-tuned models than on base models, a previously overlooked interaction. Simple prompting and supervised fine-tuning work for concept injection but not removal, and cheap textual metrics are found to correlate well with expensive LLM-as-judge evaluations.
Inference-time steering methods compared for cross-lingual factual consistency in LLMs
Researchers investigate four intervention strategies for reducing cross-lingual factual inconsistency in LLMs, where models give different factual answers depending solely on prompt language. Testing on Gemma 3 12B Instruct, they compare persona prompting, Contrastive Activation Addition (CAA), and DPO-based adapters, finding that simple persona prompting outperforms more invasive methods in efficacy, safety, and generalization. The paper also introduces a novel culturally-rooted generalization benchmark to assess whether factual interventions transfer beyond the training distribution. Results suggest cross-lingual inconsistency is partly a selection problem addressable without weight modification.
RL-based alignment improves interactivity in full-duplex spoken dialogue models
Researchers propose a post-training alignment method using reinforcement learning to improve interactivity in full-duplex spoken dialogue models, which can listen and speak simultaneously. The method addresses four canonical axes of interactivity—pause handling, turn-taking, backchanneling, and user interruption—each with axis-specific reward functions, plus an LLM-based reward to prevent semantic degradation. The approach is applied to two open-source models, Moshi and PersonaPlex, showing consistent improvements in both offline and real-time multi-turn evaluation.
LLMs Show Inverted Compositional Strengths vs. Humans on Reference Resolution Task
This paper evaluates LLMs and humans on the Personal Relation Task (Paperno 2022), distinguishing between Extensional tasks (resolving what an expression refers to) and Intensional tasks (representing structured sense/formula). The study finds that humans outperform LLMs on Extensional tasks while LLMs outperform humans on Intensional tasks—an inverted pattern of strengths. The authors argue this asymmetry reflects the absence of referential grounding in LLM training as a key gap in human-like language understanding.
LLM-Based Grammar Adaptation for Metamodel-Grammar Co-Evolution in Model-Driven Engineering
This paper proposes using LLMs to automate grammar adaptation when metamodels evolve in model-driven engineering, replacing tedious manual work and outperforming rule-based methods. Evaluated on six real-world Xtext DSLs using Claude Sonnet 4.5, ChatGPT 5.1, and Gemini 3, all three LLMs achieved 100% adaptation consistency on test DSLs versus 62-84% for rule-based approaches. A longitudinal study on QVTo showed LLMs successfully reused learned adaptations across all evolution steps without manual editing. However, on large-scale grammars (EAST-ADL, 297 rules), LLM adaptation consistency dropped well below 90%, revealing a scalability limitation.
Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructions
This paper investigates whether language models can learn the semantics of rare English constructions (e.g., 'let alone', 'much less'), constructing a novel dataset to test form-meaning pairing understanding. Testing models across parameter counts, architectures, and pretraining dataset sizes, the authors find that modestly sized open-source models can grasp Paired-Focus construction semantics, while models trained on human-scale data fail. Training dynamics analysis reveals that semantic understanding of these constructions emerges later than syntactic knowledge and correlates with gains in world knowledge more broadly.