Feedback-Aware Inference for Iterative Multi-Sample Text Generation
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| Title: | Feedback-Aware Inference for Iterative Multi-Sample Text Generation |
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| Language: | English |
| Authors: | Andreea Dutulescu (ORCID |
| Source: | Grantee Submission. 2026 7. |
| Peer Reviewed: | Y |
| Page Count: | 26 |
| Publication Date: | 2026 |
| Sponsoring Agency: | National Center for Education Research (NCER) (ED/IES) |
| Contract Number: | R305T240035 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Natural Language Processing, Man Machine Systems, Feedback (Response), Inferences, Computer Assisted Instruction, Material Development, Item Banks, Program Development, Models |
| DOI: | 10.3390/ai7050171 |
| Abstract: | Generating multiple text sequences and refining them through feedback is essential for improving the quality of outputs in many NLP tasks. While Large Language Models can leverage iterative feedback during inference, smaller models often lack this capability due to limited capacity and the absence of suitable training paradigms. In this paper, we propose a novel Feedback-Aware Inference approach that enables iterative sequence generation with integration of feedback signals. Our method allows models to generate multiple sequences, incorporate feedback from previous iterations, and refine outputs accordingly. This approach dynamically adjusts to different quality metrics, making it adaptable to various contexts and objectives. We evaluate our approach on two distinct tasks: Answer Selection for Question Generation and Keyword Generation, arguing for its generalizability and effectiveness. Results show that our method outperforms strong baselines, maintaining high performance across iterations and achieving superior results even with smaller, open-source models. |
| Abstractor: | As Provided |
| Notes: | https://github.com/upb-nlp/Feedback-Aware |
| IES Funded: | Yes |
| Entry Date: | 2026 |
| Accession Number: | ED681036 |
| Database: | ERIC |
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| Abstract: | Generating multiple text sequences and refining them through feedback is essential for improving the quality of outputs in many NLP tasks. While Large Language Models can leverage iterative feedback during inference, smaller models often lack this capability due to limited capacity and the absence of suitable training paradigms. In this paper, we propose a novel Feedback-Aware Inference approach that enables iterative sequence generation with integration of feedback signals. Our method allows models to generate multiple sequences, incorporate feedback from previous iterations, and refine outputs accordingly. This approach dynamically adjusts to different quality metrics, making it adaptable to various contexts and objectives. We evaluate our approach on two distinct tasks: Answer Selection for Question Generation and Keyword Generation, arguing for its generalizability and effectiveness. Results show that our method outperforms strong baselines, maintaining high performance across iterations and achieving superior results even with smaller, open-source models. |
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| DOI: | 10.3390/ai7050171 |
