United States

Idiomatic Expression Generation, which aims to produce idiomatic text from plain text, is a valuable yet challenging NLP task. However, existing methods suffer from the scarcity of parallel data and dependence on high-quality manual annotations. To address this, we propose an iterative LLM-SLM (Large Language Model-Small Language Model) collaborative framework — Auto-IDEA, that replaces human supervision for idiomatic expression data generation. In this self-improving cycle, the LLM constructs parallel corpora (idiomatic and plain text) via bidirectional semantic reconstruction, automatically generating &quot;Locate-Then-Polish&quot; (LTP) annotations; the SLM filters low-quality corpora while continuously enhancing its verification ability through incremental learning. We instantiate Auto-IDEA for Chinese Idiom Polishing (CIP), constructing CIP-200K, a large-scale dataset of 206K parallel sentences with LTP annotations. The Qwen3-8B fine-tuned on CIP-200K achieves a 25.2\% absolute Idiom Polishing Accuracy (IPA) improvement over a supervised fine-tuning (SFT) baseline, outperforming DeepSeek-R1 by 6.2\%. Extensive experiments (e.g., Chinese idiom cloze tests and English idiom generation tasks) and human evaluations verify the generalization and effectiveness of Auto-IDEA, demonstrating a new pathway for high-quality, annotation-free data generation through LLM-SLM collaboration.

ACL 2026

LLM-SLM Collaborative Framework of Idiomatic Expression Generation

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Idiomatic Expression Generation, which aims to produce idiomatic text from plain text, is a valuable yet challenging NLP task. However, existing methods suffer from the scarcity of parallel data and dependence on high-quality manual annotations. To address this, we propose an iterative LLM-SLM (Large Language Model-Small Language Model) collaborative framework — Auto-IDEA, that replaces human supervision for idiomatic expression data generation. In this self-improving cycle, the LLM constructs parallel corpora (idiomatic and plain text) via bidirectional semantic reconstruction, automatically generating "Locate-Then-Polish" (LTP) annotations; the SLM filters low-quality corpora while continuously enhancing its verification ability through incremental learning. We instantiate Auto-IDEA for Chinese Idiom Polishing (CIP), constructing CIP-200K, a large-scale dataset of 206K parallel sentences with LTP annotations. The Qwen3-8B fine-tuned on CIP-200K achieves a 25.2\% absolute Idiom Polishing Accuracy (IPA) improvement over a supervised fine-tuning (SFT) baseline, outperforming DeepSeek-R1 by 6.2\%. Extensive experiments (e.g., Chinese idiom cloze tests and English idiom generation tasks) and human evaluations verify the generalization and effectiveness of Auto-IDEA, demonstrating a new pathway for high-quality, annotation-free data generation through LLM-SLM collaboration.

technical paper

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Detecting pre-training data in Large Language Models (LLMs) is crucial for auditing data privacy and copyright compliance, yet it remains challenging in black-box, zero-shot settings where computational resources and training data are scarce. While existing likelihood-based methods have shown promise, they typically aggregate token-level scores using uniform weights, thereby neglecting the inherent information-theoretic dynamics of autoregressive generation. In this paper, we hypothesize and empirically validate that memorization signals are heavily skewed towards the high-entropy initial tokens, where model uncertainty is highest, and decay as context accumulates. To leverage this linguistic property, we introduce Positional Decay Reweighting (PDR), a training-free and plug-and-play framework. PDR explicitly reweights token-level scores to amplify distinct signals from early positions while suppressing noise from later ones. Extensive experiments show that PDR acts as a robust prior and can usually enhance a wide range of advanced methods across multiple benchmarks.

PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection

Large Language Models (LLMs) have demonstrated remarkable general capabilities, yet they falter in domains requiring deep strategic reasoning. A primary obstacle is the need to navigate a game tree that grows exponentially with search depth, a task for which their generative nature is ill-suited. To address this, we introduce Generative Gamer (GenGamer), a framework that trains LLMs to reason like an expert player. Instead of attempting an exhaustive search, GenGamer learns to generate a compact, pruned reasoning trajectory termed as a Dynamic Deduction. This is achieved by integrating three key strategies: action pruning based on policy confidence, state pruning via value estimation, and branch pruning inspired by alpha-beta principles. Furthermore, to train the model effectively, we propose the Deduction Tree Reward (DTR), a process-oriented mechanism that provides step-by-step feedback on the quality of the reasoning process, rather than relying solely on the final game outcome. Experiments on complex games such as Tic-Tac-Toe and Leduc Poker demonstrate that GenGamer significantly enhances the strategic capabilities of LLMs, enabling them to achieve performance that surpasses current state-of-the-art language models.

Generative Gamer: Learning Equilibrium Strategy by LLM-driven Dynamic Deduction

While Vision–language models (VLMs) interpret text-rich images effectively, they struggle with reasoning across long, multi-page documents. We present $\textbf{A}$ctive $\mathbf{L}$ong $\mathbf{D}$ocum$\mathbf{E}$nt $\mathbf{N}$avigation (ALDEN), a multi-turn reinforcement learning framework that fine-tunes VLMs as interactive agents capable of actively navigating long, visually rich documents rather than passive readers. ALDEN features a novel $\texttt{fetch}$ action that allows direct page indexing, complementing the classic $\textbf{search}$ action and better exploiting document structure. To ensure training efficiency and stability, we introduce a rule-based cross-level reward for dense supervision and a visual-semantic anchoring mechanism utilizing dual-path KL-divergence constraints. We train ALDEN on a curated corpus built from open-source datasets where trivial samples are filtered, and queries are rewritten to incentivize multi-turn navigation and fetch usage. Empirically, ALDEN achieves state-of-the-art results on five long-document benchmarks, offering a more accurate and efficient path for long-document understanding.

ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents

Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations. In this work, we provide a theoretical perspective analyzing the gradient inductive bias of MTP, supported by empirical evidence, showing that MTP promotes the convergence toward internal belief states by inducing representational contractivity via gradient coupling. However, we reveal that standard MTP often suffers from structural hallucinations, where discrete token supervision encourages illegal shortcuts in latent space that violate environmental constraints. To address this, we propose a novel method **Latent Semantic Enhancement MTP (LSE-MTP)**, which anchors predictions to ground-truth hidden state trajectories. Experiments on synthetic graphs and real-world Manhattan Taxi Ride show that LSE-MTP effectively bridges the gap between discrete tokens and continuous state representations, enhancing representation alignment, reducing structural hallucinations, and improving robustness to perturbations.

Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

Relation extraction represents a fundamental component in the process of creating knowledge graphs, among other applications. Large language models (LLMs) have been adopted as a promising tool for relation extraction, both in supervised and in-context learning settings. However, in this work we show that their performance still lags behind much smaller architectures when the linguistic graph underlying a text has great complexity. To demonstrate this, we evaluate four LLMs against a graph-based parser on six relation extraction datasets with sentence graphs of varying sizes and complexities. Our results show that the graph-based parser increasingly outperforms the LLMs, as the number of relations in the input documents increases. This makes the much lighter graph-based parser a superior choice in the presence of complex linguistic graphs.

LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs

Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities but often grapple with reliability challenges like hallucinations. While Knowledge Graphs (KGs) offer explicit grounding, existing paradigms of KG-augmented LLMs typically exhibit cognitive rigidity—applying homogeneous search strategies that render them vulnerable to instability under neighborhood noise and structural misalignment leading to reasoning stagnation. To address these challenges, we propose CoG, a training-free framework inspired by Dual-Process Theory that mimics the interplay between intuition and deliberation. First, functioning as the fast, intuitive process, the Relational Blueprint Guidance module leverages relational blueprints as interpretable soft structural constraints to rapidly stabilize the search direction against noise. Second, functioning as the prudent, analytical process, the Failure-Aware Refinement module intervenes upon encountering reasoning impasses. It triggers evidence-conditioned reflection and executes controlled backtracking to overcome reasoning stagnation. Experimental results on three benchmarks demonstrate that CoG significantly outperforms state-of-the-art approaches in both accuracy and efficiency.

CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs

Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications. 
Unlike textual data where learning semantically rich token or sentence embeddings often suffice, tables are structured combinations of columns wherein useful representations must jointly capture column’s semantics and the inter-column relationships. 
Existing models learn by linearizing the 2D table into a 1D token sequence and encoding it with pretrained language models (PLMs) such as BERT. However, this leads to limited semantic quality and weaker generalization to unseen or rare values compared to modern LLMs, and degraded structural modeling due to 2D-to-1D flattening and context-length constraints. We propose TabEmb, which directly targets these limitations by decoupling semantic encoding from structural modeling. An LLM first produces semantically rich embeddings for each column, and a graph-based module over columns then injects relationships into the embeddings, yielding joint semantic–structural representations for table annotation. Experiments show that TabEmb consistently outperforms strong baselines on different table annotation tasks. Source code and datasets are available at https://github.com/hoseinzadeehsan/TabEmb

TabEmb: Joint Semantic-Structure Embedding for Table Annotation

Direct Preference Optimization (DPO) has become a standard approach for aligning large language models with human preferences, yet existing methods treat all preference pairs uniformly during training. We identify two distinct sources of learning difficulty: Input Complexity (IC), capturing prompt understanding challenges, and Output Ambiguity (OA), measuring preference discrimination difficulty. Through systematic analysis, we demonstrate that these dimensions induce asymmetric learning dynamics, with IC-related competencies developing rapidly in early training while OA-related competencies emerge more gradually. Building on this observation, we propose DECOPO, a training framework that maintains separate, adaptive pacing schedules for each dimension. Experiments on UltraFeedback show that DECOPO achieves 42.3\% length-controlled win rate on AlpacaEval 2.0 and 7.66 on MT-Bench, outperforming curriculum baselines by 2.1\% and 0.21 points respectively, while matching full-data baseline performance with only 75\% of training samples.

What Do LLMs Learn First? Asymmetric Learning Dynamics of Input Complexity and Output Ambiguity in Preference Alignment

Multi-speaker automatic speech recognition (MASR) aims to predict ''who spoke when and what'' from multi-speaker speech, a key technology for multi-party dialogue understanding. However, most existing approaches decouple temporal modeling and speaker modeling when addressing ''when'' and ''who'': some inject speaker cues before encoding (e.g., speaker masking), which can cause irreversible information loss; others fuse identity by mixing speaker posteriors after encoding, which may entangle acoustic content with speaker identity. This separation is brittle under rapid turn-taking and overlapping speech, often leading to degraded performance. To address these limitations, we propose $TellWhisper$, a unified framework that jointly models speaker identity and temporal within the speech encoder. Specifically, we design $TS\text{-}RoPE$, a time-speaker rotary positional encoding: time coordinates are derived from frame indices, while speaker coordinates are derived from speaker activity and pause cues. By applying region-specific rotation angles, the model explicitly captures per-speaker continuity, speaker-turn transitions, and state dynamics, enabling the attention mechanism to simultaneously attend to ''when'' and ''who''. Moreover, to estimate frame-level speaker activity, we develop $Hyper\text{-}SD$, which casts speaker classification in hyperbolic space to enhance inter-class separation and refine speaker-activity estimates. Extensive experiments demonstrate the effectiveness of the proposed approach. The project webpage is available at https://walker-hyf.github.io/TellWhisper.

TellWhisper: Tell Whisper Who Speaks When

Temporal reasoning is crucial for large language models (LLMs) to understand event concurrency and complex temporal interactions in natural language. Recent approaches rely on the LLM to infer temporal relations between events and largely overlook the inherent structural nature of temporal relationships. In this work, we propose ODL-TempLLM (Ontology-Guided and Description Logic–Constrained Temporal Reasoning with LLMs), a novel paradigm for temporal reasoning with LLMs that shifts focus from internal inference to the explicit modeling of temporal structure. ODL-TempLLM leverages ontology learning to explicitly construct structured temporal knowledge, employs a symbolic reasoner to deductively reason about temporal relations and uses logic-constrained retrieval augmentation to obtain relevant facts.Experiments results evaluated across there datasets via various LLM backbones show that our method outperforms state-of-the-art methods by 2.07–31.83 F1 points and 1.00–30.73 EM points, exhibiting strong generalization and highlighting the potential of explicit temporal reasoning.

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