Syntactic Control of Language Models by Posterior Inference

• URL: http://arxiv.org/abs/2506.07154v1
• Date: Sun, 08 Jun 2025 14:01:34 GMT
• Title: Syntactic Control of Language Models by Posterior Inference
• Authors: Vicky Xefteri, Tim Vieira, Ryan Cotterell, Afra Amini,
• Abstract summary: Controlling the syntactic structure of text generated by language models is valuable for applications requiring clarity, stylistic consistency, or interpretability.<n>We argue that sampling algorithms based on the posterior inference can effectively enforce a target constituency structure during generation.<n>Our approach combines sequential Monte Carlo, which estimates the posterior distribution by sampling from a proposal distribution, with a syntactic tagger that ensures that each generated token aligns with the desired syntactic structure.
• Score: 53.823006836309695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Controlling the syntactic structure of text generated by language models is valuable for applications requiring clarity, stylistic consistency, or interpretability, yet it remains a challenging task. In this paper, we argue that sampling algorithms based on the posterior inference can effectively enforce a target constituency structure during generation. Our approach combines sequential Monte Carlo, which estimates the posterior distribution by sampling from a proposal distribution, with a syntactic tagger that ensures that each generated token aligns with the desired syntactic structure. Our experiments with GPT2 and Llama3-8B models show that with an appropriate proposal distribution, we can improve syntactic accuracy, increasing the F1 score from $12.31$ (GPT2-large) and $35.33$ (Llama3-8B) to about $93$ in both cases without compromising the language model's fluency. These results underscore both the complexity of syntactic control and the effectiveness of sampling algorithms, offering a promising approach for applications where precise control over syntax is essential.

Related papers

• Ensembling Language Models with Sequential Monte Carlo [48.149136054981334]
  We introduce a unified framework for composing $K$ language models into $f$-ensemble distributions.<n>We show that better posterior approximations can yield better ensemble performance.
  arXiv  Detail & Related papers  (2026-03-05T17:54:31Z)
• Analogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction [2.3857044225736224]
  We develop a computational approach implementing three cognitive-inspired principles: analogical structure, contrastive learning, and minimal contextual cues.<n>We test this approach with structured completion tasks where models identify correct sentence completions from analogical patterns with contrastive alternatives.<n>Our results show that analogical paradigm organization enables competitive linguistic rule learning with orders of magnitude less data than conventional approaches require.
  arXiv  Detail & Related papers  (2025-11-13T16:04:46Z)
• Generative Reasoning Recommendation via LLMs [48.45009951684554]
  Large language models (LLMs) face fundamental challenges in functioning as generative reasoning recommendation models (GRRMs)<n>This work explores how to build GRRMs by adapting pre-trained LLMs, which achieves a unified understanding-reasoning-prediction manner for recommendation tasks.<n>We propose GREAM, an end-to-end framework that integrates three components: Collaborative-Semantic Alignment, Reasoning Curriculum Activation, and Sparse-Regularized Group Policy Optimization.
  arXiv  Detail & Related papers  (2025-10-23T17:59:31Z)
• Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings [25.565372681837697]
  We introduce a Multi-Granularity Hard-negative (MGH) synthesis framework to generate diverse negative samples with varying levels of similarity with the query.<n>We also propose an Anchor Token Aware (ATA) pooling method to improve text embedding accuracy without increasing model complexity.
  arXiv  Detail & Related papers  (2025-08-31T13:24:48Z)
• Large Language Model-Based Automatic Formulation for Stochastic Optimization Models [0.0]
  This paper presents the first integrated systematic study on the performance of large language models (LLMs)<n>We design several prompts that guide ChatGPT through structured tasks using chain-of- thought and modular reasoning.<n>Across a diverse set of problems, GPT-4-Turbo outperforms other models in partial score, variable matching, objective accuracy, with cot_s_instructions emerging as the most effective prompting strategies.
  arXiv  Detail & Related papers  (2025-08-24T03:31:25Z)
• Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling [90.86991492288487]
  evaluating constraint on every token can be prohibitively expensive.<n> LCD can distort the global distribution over strings, sampling tokens based only on local information.<n>We show that our approach is superior to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-04-07T18:30:18Z)
• UMB@PerAnsSumm 2025: Enhancing Perspective-Aware Summarization with Prompt Optimization and Supervised Fine-Tuning [8.095763327154335]
  We present our approach to the PerAnsSumm Shared Task, which involves perspective span identification and perspective-aware summarization.<n>For span identification, we adopt ensemble learning that integrates three transformer models through averaging to exploit individual model strengths.<n>For summarization, we design a suite of Chain-of-Thought (CoT) prompting strategies that incorporate keyphrases and guide information to structure summary generation into manageable steps.
  arXiv  Detail & Related papers  (2025-03-14T06:29:51Z)
• Combinatorial Optimization via LLM-driven Iterated Fine-tuning [47.66752049943335]
  We present a novel way to integrate flexible, context-dependent constraints into optimization by leveraging Large Language Models (LLMs)<n>Our framework balances locally constraints with rigorous global optimization more effectively than baseline sampling methods.
  arXiv  Detail & Related papers  (2025-03-10T04:58:18Z)
• In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement [71.60563181678323]
  Large language models (LLMs) have achieved great success across diverse tasks, and fine-tuning is sometimes needed to further enhance generation quality.<n>To handle these challenges, a direct solution is to generate high-confidence'' data from unsupervised downstream tasks.<n>We propose a novel approach, pseudo-supervised demonstrations aligned prompt optimization (PAPO) algorithm, which jointly refines both the prompt and the overall pseudo-supervision.
  arXiv  Detail & Related papers  (2024-10-04T03:39:28Z)
• Graph-Structured Speculative Decoding [52.94367724136063]
  Speculative decoding has emerged as a promising technique to accelerate the inference of Large Language Models. We introduce an innovative approach utilizing a directed acyclic graph (DAG) to manage the drafted hypotheses. We observe a remarkable speedup of 1.73$times$ to 1.96$times$, significantly surpassing standard speculative decoding.
  arXiv  Detail & Related papers  (2024-07-23T06:21:24Z)
• Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution [67.9215891673174]
  We propose score entropy as a novel loss that naturally extends score matching to discrete spaces. We test our Score Entropy Discrete Diffusion models on standard language modeling tasks.
  arXiv  Detail & Related papers  (2023-10-25T17:59:12Z)
• Minimally-Supervised Speech Synthesis with Conditional Diffusion Model and Language Model: A Comparative Study of Semantic Coding [57.42429912884543]
  We propose Diff-LM-Speech, Tetra-Diff-Speech and Tri-Diff-Speech to solve high dimensionality and waveform distortion problems. We also introduce a prompt encoder structure based on a variational autoencoder and a prosody bottleneck to improve prompt representation ability. Experimental results show that our proposed methods outperform baseline methods.
  arXiv  Detail & Related papers  (2023-07-28T11:20:23Z)
• On the N-gram Approximation of Pre-trained Language Models [17.764803904135903]
  Large pre-trained language models (PLMs) have shown remarkable performance across various natural language understanding (NLU) tasks. This study investigates the potential usage of PLMs for language modelling in Automatic Speech Recognition (ASR) We compare the application of large-scale text sampling and probability conversion for approximating GPT-2 into an n-gram model.
  arXiv  Detail & Related papers  (2023-06-12T06:42:08Z)
• Syntactically Robust Training on Partially-Observed Data for Open Information Extraction [25.59133746149343]
  Open Information Extraction models have shown promising results with sufficient supervision. We propose a syntactically robust training framework that enables models to be trained on a syntactic-abundant distribution.
  arXiv  Detail & Related papers  (2023-01-17T12:39:13Z)
• Exemplar-Controllable Paraphrasing and Translation using Bitext [57.92051459102902]
  We adapt models from prior work to be able to learn solely from bilingual text (bitext) Our single proposed model can perform four tasks: controlled paraphrase generation in both languages and controlled machine translation in both language directions.
  arXiv  Detail & Related papers  (2020-10-12T17:02:50Z)

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.