Papers with general language models
ProcessBench: Identifying Process Errors in Mathematical Reasoning (2025.acl-long)
Copied to clipboard
Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin
| Challenge: | Existing models fail to generalize to more challenging math problems, authors say . existing benchmarks related to assessing language models' reasoning process are limited . |
| Approach: | They propose a tool to measure language models' ability to identify erroneous steps in reasoning . they use two types of models: process reward models and critic models . |
| Outcome: | The proposed model outperforms existing models in evaluating language models' reasoning process . the best open-source model has demonstrated the critique capability competitive with the proprietary model . |
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for zero-shot dialogue state tracking (DST) ignore unlabelled data in the target domain. |
| Approach: | They propose to transform zero-shot dialogue state tracking into few-shot DST by utilising unlabelled data via joint and self-training methods. |
| Outcome: | The proposed method improves joint goal accuracy by 8% on general language models in zero-shot scenarios, and can be used in many domains. |
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2022.naacl-main)
Copied to clipboard
Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi
| Challenge: | Prior studies suggested pre-trained language models possess limited understanding of commonsense knowledge despite otherwise stellar performance on leaderboards. |
| Approach: | They propose a framework that uses larger models to teach smaller models by distilling knowledge symbolically as text in addition to the neural model. |
| Outcome: | The proposed framework is based on a general language model teacher's commonsense knowledge graphs and a neural commonsensing model surpassing the teacher model's in all three criteria. |