Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Large Reasoning Models (LRMs) such as OpenAI o1 and DeepSeek-R1 have shown remarkable reasoning capabilities by scaling test-time compute and generating long Chain-of-Thought (CoT). Distillation post-training on LRMs-generated data is a straightforward yet effective method to enhance the reasoning abilities of smaller models, but faces a critical bottleneck: we found that distilled long CoT data poses learning difficulty for small models and leads to the inheritance of biases (i.e., formalistic long-time thinking) when using Supervised Fine-tuning (SFT) and Reinforcement Learning (RL) methods. To alleviate this bottleneck, we propose constructing data from scratch using Monte Carlo Tree Search (MCTS). We then exploit a set of CoT-aware approaches, including Thoughts Length Balance, Fine-grained DPO, and Joint Post-training Objective, to enhance SFT and RL on the MCTS data. We conducted evaluation on various benchmarks such as math (GSM8K, MATH, AIME). instruction-following (Multi-IF) and planning (Blocksworld), results demonstrate our CoT-aware approaches substantially improve the reasoning performance of distilled models compared to standard distilled models via reducing the hallucinations in long-time thinking.

While non-autoregressive sign language translation (NASLT) has the advantage in inference speed, the translation quality of NASLT models lags significantly behind that of the state-of-the-art (SOTA) autoregressive sign language translation (ASLT) models. To bridge the quality gap, we exploit glosses to unlock the potential of NASLT models. Concretely, we propose Gloss-enhanced Levenshtein Transformer (GLevT) for sign language translation (SLT), which takes glosses as initial sequences for editing into texts. In particular, to alleviate the inconsistency between training and inference of GLevT, which is introduced by glosses, we propose a dual-centric learning policy and a keyframe-based gloss replacement method for training, further improving the translation quality of GLevT. Experiments on CSL-Daily demonstrate that GLevT outperforms other NASLT models by approximately 4 points in BLEU and ROUGE scores, while achieving performance comparable to the SOTA ASLT models with a 3.46~5.26× inference speed-up. Furthermore, we extend GLevT to gloss-free SLT, achieving performance comparable to SOTA large models, despite having only 49M parameters. We release code at https://github.com/XMUDeepLIT/GLevT.

Keyphrase generation (KPG) aims to automatically generate a collection of phrases representing the core concepts of a given document.The dominant paradigms in KPG include ONE2SEQ and ONE2SET.Recently, there has been increasing interest in applying large language models (LLMs) to KPG.Our preliminary experiments reveal that it is challenging for a single model to excel in both recall and precision.Further analysis shows that: 1) the ONE2SET paradigm owns the advantage of high recall, but suffers from improper assignments of supervision signals during training; 2) LLMs are powerful in keyphrase selection, but existing selection methods often make redundant selections.Given these observations, we introduce a generate-then-select framework decomposing KPG into two steps, where we adopt a ONE2SET-based model as generator to produce candidates and then use an LLM as selector to select keyphrases from these candidates.Particularly, we make two important improvements on our generator and selector: 1) we design an Optimal Transport-based assignment strategy to address the above improper assignments; 2) we model the keyphrase selection as a sequence labeling task to alleviate redundant selections.Experimental results on multiple benchmark datasets show that our framework significantly surpasses state-of-theart models, especially in absent keyphrase prediction.We release our code at https:// github.

Liang Zhang, Zhen Yang, Biao Fu, Ziyao Lu, Liangying Shao, Shiyu Liu, Fandong Meng, Jie Zhou, Xiaoli Wang, Jinsong Su. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.