Papers with WebGPT
RELIC: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples (2025.findings-emnlp)
Copied to clipboard
Soumya Suvra Ghosal, Vaibhav Singh, Akash Ghosh, Soumyabrata Pal, Subhadip Baidya, Sriparna Saha, Dinesh Manocha
| Challenge: | a new reward model for low-resource Indic languages is proposed . a preference-based training approach is prohibitively expensive, authors say . |
| Approach: | a new in-context learning framework is proposed to train a retriever to select in-constext examples from low-resource Indic languages. |
| Outcome: | a new in-context learning framework for reward modeling in low-resource Indic languages is developed . the proposed framework outperforms existing examples on three preference datasets . |
How Do We Answer Complex Questions: Discourse Structure of Long-form Answers (2022.acl-long)
Copied to clipboard
| Challenge: | Recent work explored long-form answers, where answers are free-form texts consisting of multiple sentences. |
| Approach: | They develop an ontology of six sentence-level functional roles for long-form answers . they annotate 3.9k sentences in 640 answer paragraphs and train a strong classifier . |
| Outcome: | The proposed model-generated answers agree less with model-driven answers than human-written answers. |
How Credible Is an Answer From Retrieval-Augmented LLMs? Investigation and Evaluation With Multi-Hop QA (2025.coling-main)
Copied to clipboard
| Challenge: | Retrieval-augmented large language models (RaLLMs) are reshaping knowledge acquisition, offering long-form, knowledge-grounded answers through advanced reasoning and generation capabilities. |
| Approach: | They propose a benchmarking system to evaluate RaLLMs' correctness and Groundedness to determine their reliability in multi-hop question-answering tasks. |
| Outcome: | The proposed model-based evaluation pipeline for multi-hop question-answering tasks reveals that the model generates inaccuracies when dealing with flawed or partial knowledge. |
WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)
Copied to clipboard
Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Long-form question answering requires two procedures: information retrieval and information synthesis. |
| Approach: | They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time . |
| Outcome: | The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset . |