arXiv:2606. 14943v1 Announce Type: cross Abstract: Causal Transformers model sequences through an autoregressive factorization of the joint distribution, which enables efficient left-to-right decoding and conditional likelihood computation.
arXiv:2602.00612v3 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) have demonstrated promising generative capabilities and are increasingly used to produce formal languages d...
arXiv:2607. 20483v1 Announce Type: new Abstract: Constraining the generation of autoregressive large language models (LLMs) is an important component of integrating language models into formal systems.
RECAST is a new framework that generates datasets with far more constraints per example than existing benchmarks, aiming to push large language models (LLMs) to better follow complex instructions. The authors built RECAST-30K, a 30,000‑instance dataset covering 19 constraint types extracted from real prompt‑response pairs, and showed that fine‑tuning on it improves LLMs’ ability to handle complex tasks without harming general performance. RECAST also provides rule‑based and LLM‑based validators for automatic constraint verification, enabling reward‑based reinforcement learning to further enhance model performance on challenging tasks.
arXiv:2608. 05493v1 Announce Type: cross Abstract: Language models (LMs) are increasingly used to interact with external services via programs written in domain-specific languages (DSLs).
The paper introduces Grammar‑Aligned Decoding (GAD), addressing the issue that conventional grammar‑constrained decoding (GCD) can distort a large language model’s probability distribution, yielding grammatical but low‑likelihood outputs. GAD proposes an adaptive sampling method, Approximate Expected Futures (ASAp), which uses prior samples to over‑approximate future grammaticality, ensuring outputs remain both grammatical and faithful to the model’s conditional probabilities. Experiments on code generation and structured NLP tasks demonstrate that ASAp often produces higher‑likelihood outputs than existing GCD techniques while still enforcing the required grammatical constraints.
arXiv:2602. 16481v2 Announce Type: replace Abstract: Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions.
arXiv:2607. 20456v1 Announce Type: cross Abstract: Large language models excel at code generation for mainstream programming languages but struggle with rare, domain-specific languages such as MiniZinc, a constraint modeling language for combinatorial problems.
arXiv:2606. 04535v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally support format-constrained tasks like parseable JSON or reasoning templates.
arXiv:2405.15454v5 Announce Type: replace Abstract: The prevalence of Large Language Models (LLMs) in critical applications highlights the need for controlled language generation methods that are bot...
The paper introduces UC-Bench, a human‑annotated benchmark for detecting user‑side implicit conflicts in Human‑LLM dialogue, a problem largely overlooked compared to LLM‑side conflicts. Experiments show current LLMs struggle with these conflicts, especially when they stem from implicit incompatibilities in dialogue history. To address this, the authors propose SynUC, a constraint‑guided data synthesis method that generates a new training set, UC‑Data, which improves performance of lightweight LLMs on UC‑Bench compared to larger general‑purpose models and existing synthesis approaches.
Narcissus is a program synthesizer that uses context‑aware large language model (LLM) approximations to guide enumerative search. Unlike prior methods that convert LLM proposals into rule frequencies and lose structural information, Narcissus retains proposals as syntax trees and scores each program expansion based on its surrounding context, ensuring every rule remains reachable. Across five domains and two search backends, it consistently outperforms static guidance and LLM re‑prompting, solving 40% of ARC tasks that raw proposals only solve 13%, all without any LLM calls during search.