Papers with refinement stage
Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances in model distillation show that data from advanced reasoning models can effectively train smaller student models. |
| Approach: | They propose a method to use both positive and negative distilled reasoning traces to maximize LLM reasoning performance in offline settings. |
| Outcome: | The proposed model outperforms existing methods in the distillation context. |
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding (2021.emnlp-main)
Copied to clipboard
| Challenge: | Dialogue systems that generate factually incorrect responses are often unfitful and hallucinate factuality invalid. |
| Approach: | They propose a method to improve faithfulness and reduce hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph. |
| Outcome: | The proposed approach improves faithfulness and reduces hallucination of dialogue systems to known facts . it leverages a token-level fact critic to identify plausible sources of hallucinism . |
From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for parsing knowledge-base questions into executable logical forms have not been successful on complex KBQA. |
| Approach: | They propose a new semantic parser called KoPL to model the reasoning processes . they propose 'parse-execute-refine' paradigm to unlock reasoning ability . |
| Outcome: | The proposed parser performs better than the state-of-the-art on complex KBQA . the proposed parsed-execute-refine paradigm can model complex reasoning steps . |
Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement (2025.acl-long)
Copied to clipboard
Maosongcao Maosongcao, Taolin Zhang, Mo Li, Chuyu Zhang, Yunxin Liu, Conghui He, Haodong Duan, Songyang Zhang, Kai Chen
| Challenge: | Existing high-quality human-annotated SFT data is a bottleneck for Large Language Models (LLMs). |
| Approach: | They propose a two-stage synthetic data generation framework that incorporates World Knowledge Trees and Self-Reflection Refinement to produce high-quality SFT data at scale. |
| Outcome: | The proposed model fine-tuned on 20K condor-generated samples achieves superior performance compared to instruct model trained with RLHF. |