Papers with Feedback
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation (P18-4)
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| Challenge: | Money laundering (AML) is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets. |
| Approach: | They propose a framework that uses deep learning to augment AML monitoring and investigation . money laundering is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets . |
| Outcome: | The proposed framework reduces time and cost by 30% compared to existing methods . money laundering is the world's third largest "industry" |
An Online Readability Leveled Arabic Thesaurus (2020.coling-demos)
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| Challenge: | a small minority of dictionaries specify the readability level of their words, let alone their lexical relations with other words. |
| Approach: | They propose to use Arabic lemmas, roots, English glosses, related Arabic words and phrases to provide a readability leveled Arabic thesaurus interface. |
| Outcome: | The proposed system provides the user with lemmas, roots, English glosses, related Arabic words and phrases, and readability on a five-level readability scale. |
FEAT-writing: An Interactive Training System for Argumentative Writing (2025.coling-demos)
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| Challenge: | Argumentative writing is a critical skill for academic success, but many students struggle to develop these skills. |
| Approach: | They developed an online system that provides students with automated feedback and exercises for argumentative writing. |
| Outcome: | The proposed system improves argumentative writing quality among native English speakers and english-as-a-foreign-language learners. |
A Dataset for Investigating the Impact of Feedback on Student Revision Outcome (2020.lrec-1)
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| Challenge: | Despite numerous studies on the kinds of feedback that can best promote learning, this question remains an open debate in the area of Second Language Acquisition (SLA). |
| Approach: | They annotate a corpus of student-written sentences with teacher feedback provided for the errors. |
| Outcome: | The proposed annotation scheme and the teacher feedback dataset are based on student-written sentences in their original and revised versions with teacher feedback provided for the errors. |
Synthesizing Human Gaze Feedback for Improved NLP Performance (2023.eacl-main)
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| Challenge: | Prior work on eye tracking and NLP reveals that human scanpaths can aid in understanding and performance of NLP models. |
| Approach: | They propose a model for generating human scanpaths over text that approximates meaningful cognitive signals in human gaze patterns. |
| Outcome: | The proposed model can approximate meaningful cognitive signals in human gaze patterns. |
A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)
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Hanyu Lai, Xiao Liu, Junjie Gao, Jiale Cheng, Zehan Qi, Yifan Xu, Shuntian Yao, Dan Zhang, Jinhua Du, Zhenyu Hou, Xin Lv, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages. |
| Approach: | They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies. |
| Outcome: | The proposed model can be used to understand and generate human natural languages. |
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data (2023.emnlp-main)
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| Challenge: | Despite the promising potential of chat models, they are only accessible through restricted APIs, creating barriers for new research and progress in the field. |
| Approach: | They propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself. |
| Outcome: | The proposed pipeline generates a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself, simulating both user and AI responses. |
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs (2023.acl-long)
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| Challenge: | Despite their success, even the largest language models make mistakes. |
| Approach: | They propose a framework where one language model can generate critiques to improve its peer's performance. |
| Outcome: | The proposed framework improves the performance of a fixed model 200 times its size by 10% over other models. |
Feedback to Reasoning: LLM-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning (2026.findings-acl)
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Wenhan Gao, Xiran Fan, Chin-Chia Michael Yeh, Jiarui Sun, Yuzhong Chen, Menghai Pan, Mahashweta Das, Yi Liu
| Challenge: | Existing methods for molecular optimization do not leverage domain feedback and historical knowledge with reasoning traces and chemical insights. |
| Approach: | They propose a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback. |
| Outcome: | The proposed framework transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience. |
Learning from a Friend: Improving Event Extraction via Self-Training with Feedback from Abstract Meaning Representation (2023.findings-acl)
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| Challenge: | Existing data scarcity hinders the progress of event extraction, authors say . ACE-052 has 10 of the 33 event types with less than 80 annotations, authors claim . |
| Approach: | They propose a self-training with feedback framework that leverages large-scale unlabeled data to acquire feedback for each new event prediction from the unlabed data. |
| Outcome: | The proposed framework improves event extraction models even when unlabeled data are unavailable. |
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement (2024.findings-acl)
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| Challenge: | OpenCodeInterpreter-33B provides a high level of performance for code generation, executing, and iterative refinement. |
| Approach: | They propose a family of open-source code systems for generating, executing, and iteratively refining code. |
| Outcome: | The OpenCodeInterpreter-33B performs well on humanEval, MBPP, and EvalPlus benchmarks. |
HelpSteer3: Human-Annotated Feedback and Edit Data to Empower Inference-Time Scaling in Open-Ended General-Domain Tasks (2025.acl-long)
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Zhilin Wang, Jiaqi Zeng, Olivier Delalleau, Daniel Egert, Ellie Evans, Hoo-Chang Shin, Felipe Soares, Yi Dong, Oleksii Kuchaiev
| Challenge: | Inference-Time Scaling is critical to the success of recent models such as OpenAI o1 and DeepSeek R1 . however, many techniques require tasks to have answers that can be verified . |
| Approach: | They use data to train dedicated Feedback and Edit Models capable of inference-time scaling for open-ended tasks. |
| Outcome: | The proposed model can reach SoTA performance on Arena Hard at 92.7 as of 5 Mar 2025. |
Iterative Repair with Weak Verifiers for Few-shot Transfer in KBQA with Unanswerability (2025.findings-acl)
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| Challenge: | Existing models for KBQA with unanswerable questions are inadequate for real-world applications. |
| Approach: | They propose a task of few-shot transfer for KBQA with unanswerable questions that extends FuSIC-KBQA to include feedback for unanswered questions. |
| Outcome: | The proposed model outperforms suitable adaptations of multiple LLM-based and supervised SoTA models on the task while establishing a new performance for answerable few-shot transfer as well. |
Creating Grammar Teaching Material for Endangered Languages with Hybrid Grammar Induction (2026.findings-acl)
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| Challenge: | Existing methods for creating grammar lessons are labor-intensive and often fall to teachers who lack formal training in grammar. |
| Approach: | They propose a hybrid grammar-induction method that uses typological priors, Bayesian inference, constrained LLM reasoning and retrieval from sparse corpora to generate topic-specific grammar lessons. |
| Outcome: | The proposed method can produce coherent and useful lessons with better quality when modest explanatory evidence is available. |
Feedback Is The Key for Automated Survey Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a promising foundation for literature surveys, but guiding them to generate accurate, reliable content remains a fundamental challenge. |
| Approach: | They propose a feedback-driven framework that incorporates feedback across three dimensions: outline feedback for structural clarity, citation feedback for evidence validation, and content feedback for readability and analytical depth. |
| Outcome: | The proposed framework significantly improves both citation and content quality, demonstrating feedback as the critical mechanism for automatic survey generation. |