Papers by Shangjian Yin
Aligning Large Language Models via Fully Self-Synthetic Data (2026.acl-long)
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| Challenge: | Existing approaches to reinforcement learning from human feedback (RLHF) require expensive human-annotated datasets and proprietary models like GPT-4 to annotate preference pairs. |
| Approach: | They propose a self-synthetic framework for LLM alignment where all training data, including prompts (i.e., user queries), responses, and preferences, are generated by the model itself. |
| Outcome: | The proposed framework enhances the model’s chat capabilities on standard benchmarks like AlpacaEval 2.0 while maintaining strong performance on downstream objective tasks. |
ECLM: Entity Level Language Model for Spoken Language Understanding with Chain of Intent (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance, but their application to spoken language understanding remains challenging. |
| Approach: | They propose an Entity-level Language Model framework which reformulates slot-filling as an entity recognition task and introduces a new concept, Chain of Intent, to enable step-by-step multi-intent recognition. |
| Outcome: | The proposed framework outperforms strong baselines such as Uni-MIS and achieves gains of 3.7% and 3.1% on MixATIS and MixSNIPS. |
From Individual to Common: An Early Exploration of Consensus in Non-verifiable Data for Balanced Preference Optimization (2026.acl-long)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated remarkable effectiveness in boosting the objective performance of Large Language Models (LLMs). |
| Approach: | They propose a dataset where response pairs differ only by subtle nuances and a model with a non-verifiable dataset. |
| Outcome: | The proposed model outperforms models trained on data with explicit quality gaps while maintaining objective capabilities. |
MIDLM: Multi-Intent Detection with Bidirectional Large Language Models (2025.coling-main)
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| Challenge: | Existing models that use autoregressive architectures restrict the sharing of token information within a sentence. |
| Approach: | They propose a framework that integrates intent number detection and multi-intent selection to enable autoregressive LLMs to leverage bidirectional information awareness through post-training. |
| Outcome: | The proposed framework outperforms existing models and pretrained baselines in the multi-intent detection task. |