arXiv AI
The Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models
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arXiv Computation and Language
Sep 25
Scoring Both Directions: LLMs realize the MRS they cannot reliably parse
The paper evaluates two large language models, Claude Sonnet 4.5 and Claude Opus 5, on the bidirectional English Resource Grammar (ERG) tasks of generating English from Minimal Recursion Semantics (MRS) and parsing English into MRS. In generation, Opus achieves 76.3 BLEU—surpassing a 72k‑pair trained system and matching a million‑pair system—while Sonnet scores 65.7 BLEU, rising to 69.6 when selecting from ACE’s candidates. In parsing, both models lag behind ACE, attaining only 57.2 and 65.5 F₁ respectively, with exact‑match on about 1 % of sentences, highlighting that high generation scores do not guarantee accurate semantic parsing.
arXiv AI
Aug 18
Structural Generalization on SLOG without Hand-Written Rules
arXiv:2604. 26157v4 Announce Type: replace-cross Abstract: Structural generalization in semantic parsing requires systems to apply learned compositional rules to novel structural combinations.
arXiv Computation and Language
Oct 21
From Abstraction to Action: DSLs for Natural Language Abstraction Interpretation and Execution
The paper introduces domain-specific languages (DSLs) as an explicit representation layer to bridge the gap between high-level natural-language abstractions and concrete actions. Experiments on the visually-grounded “Hexagons” task and the “Text2CAD” benchmark show that code generation using a human-curated DSL yields dramatic performance gains, especially at higher abstraction levels, while automatically induced DSLs only partially recover these gains. The study highlights that the right representation can close much of the performance gap caused by abstraction, but that automatically learning such representations remains a challenge.
arXiv Computation and Language
3d ago
Defragmenting Language Models: An Interpretability-based Approach for Vocabulary Expansion
arXiv:2604.16656v2 Announce Type: replace Abstract: All languages are equal; when it comes to tokenization, some are more equal than others. Tokens are the hidden currency that dictates the cost and...
arXiv Machine Learning
Jun 25
Weave of Formal Thought
arXiv:2606. 25987v1 Announce Type: cross Abstract: Large language models (LLMs) attain remarkable surface fluency on code, yet they neither formally guarantee the syntactic validity of their output nor leverage the hierarchical structure defining the target language.
arXiv Computation and Language
Oct 23
Verbalizing Activation Differences with Natural Language Autoencoders
The paper investigates natural language autoencoders (NLAs) as tools for verbalizing activation differences in large language models. It examines whether NLAs capture compositional structure, generalize to fine‑tuned models, recover fine‑tuning objectives, and can be specialized for model diffing. Experiments across four models show that NLAs largely succeed in these tasks, though they do not yet match state‑of‑the‑art diffing methods on emergent misalignment detection.