Papers with generative task
A Generative Approach for Mitigating Structural Biases in Natural Language Inference (2022.starsem-1)
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| Challenge: | Natural language inference datasets contain artifacts and biases that allow models to perform poorly by using a biased subset of the input without considering the remainder features. |
| Approach: | They reformulate a natural language inference task as a generative task . they find that this approach is highly robust to large amounts of bias . |
| Outcome: | The proposed model is highly robust to large amounts of bias. |
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures (2024.acl-long)
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Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O’Gorman, Nalini Singh, Andrew McCallum, Xiang Li
| Challenge: | Existing commonsense evaluations are often posed as multiple-choice questions, allowing models to exploit systematic biases. |
| Approach: | They propose a generative task that evaluates common sense via multiple open-ended generations and a method that strongly correlates with human judgments. |
| Outcome: | The proposed method outperforms strong language model baselines on a dataset of human and machine common sense. |
CE-VDG: Counterfactual Entropy-based Bias Reduction for Video-grounded Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing methods to reduce question-related bias in video-grounded dialogue generation (VDG) however, the dataset often contains inherent bias, which can cause VDG models to learn spurious correlations between questions and answers. |
| Approach: | They propose to extend the counterfactual reasoning from the information entropy perspective to the generative task, which can effectively reduce the question-related bias in the auto-regressive generation task. |
| Outcome: | The proposed method can reduce question-related bias in the auto-regressive generation task by using counterfactual entropy as an external loss. |
Generating Mental Health Transcripts with SAPE (Spanish Adaptive Prompt Engineering) (2024.naacl-long)
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| Challenge: | Large language models can generate synthetic data resembling real-world data, but their generative performance depends on the quality of the prompt used to instruct the model. |
| Approach: | They propose a Spanish Adaptive Prompt Engineering method that uses genetic algorithms to generate and select prompts that resemble real-world data. |
| Outcome: | The proposed method produces Spanish therapy transcripts that more closely resemble authentic therapy transcript compared to other prompt engineering techniques that are based on Reflexion and Chain-of-Thought. |
Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection (2024.naacl-long)
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| Challenge: | Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data. |
| Approach: | They propose a scoring approach that encapsulates three primary dimensions of summarization model quality. |
| Outcome: | The proposed method reduces reliance on human-labeled data and improves the performance of summarization models. |
Modeling Preconditions in Text with a Crowd-sourced Dataset (2020.findings-emnlp)
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Heeyoung Kwon, Mahnaz Koupaee, Pratyush Singh, Gargi Sawhney, Anmol Shukla, Keerthi Kumar Kallur, Nathanael Chambers, Niranjan Balasubramanian
| Challenge: | Existing methods for modeling preconditions in text are limited due to the lack of large scale labeled data grounded in text. |
| Approach: | They propose a crowd-sourced annotation of preconditions between event pairs in newswire that is larger than prior annotations. |
| Outcome: | The proposed model outperforms existing models on two task sets, showing that precondition knowledge is not easily accessible in LM-derived representations alone. |
PhysPRM: A Generative Process Reward Model with Fine-grained Diagnosis for Physics Problem Solving (2026.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) struggle with physics problem solving due to difficulties in decoding implicit constraints and maintaining physical consistency. |
| Approach: | They propose a Generative PRM that treats evaluation as a generative task . it produces fine-grained diagnoses comprising critiques, final judgments, and specific error types . |
| Outcome: | The proposed model improves performance across seven benchmarks in Best-of-N and critique refinement strategies. |
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation (2022.emnlp-main)
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| Challenge: | generative multilingual models fine-tuned on English forget to generate non-English data when labeled data is only available in English . generative models fine tuned on English fail to generate multilingual summarization tasks when labeling data is available in other languages . |
| Approach: | They propose to use prompt tuning to overcome catastrophic forgetting in a generative task in . they assume a strict setting with no parallel data or machine translation . |
| Outcome: | The proposed method can overcome catastrophic forgetting to enable zero-shot cross-lingual generation. |
LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Models (2024.emnlp-main)
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| Challenge: | Existing model-based guardrails have not been designed for resource-constrained computational portable devices such as mobile phones. |
| Approach: | They propose a parameter-efficient guardrail adaptation method that relies on knowledge sharing between LLMs and guardrail models to adapt to content moderation tasks. |
| Outcome: | The proposed method outperforms existing guardrail methods with lower parameter overhead and higher accuracy on the generative task. |
ConText-LE: Cross-Distribution Generalization for Longitudinal Experiential Data via Narrative-Based LLM Representations (2025.findings-emnlp)
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| Challenge: | Longitudinal experiential data offers rich insights into dynamic human states, yet building models that generalize across diverse contexts remains challenging. |
| Approach: | They propose a framework that investigates text representation strategies and output formulations to maximize large language model cross-distribution generalization for behavioral forecasting. |
| Outcome: | The proposed framework achieves 12.28% improvement in out-of-distribution accuracy and 11.99% improvement in F1 scores over binary classification methods. |
WSDPO: A Generative Word Sense Disambiguation Framework with Chain-of-Thought and Preference Optimization (2026.acl-long)
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Kunpeng Kang, Shuaimin Li, Kaiyuan Zhang, Luyang Zhang, Jiasheng Si, Bing Xu, Kehai Chen, Muyun Yang, Wenpeng Lu
| Challenge: | Word sense disambiguation (WSD) is a fundamental task in natural language processing. |
| Approach: | They propose a training framework for generative WSD with chain-of-thought (CoT) and preference optimization. |
| Outcome: | The proposed framework achieves significant performance gains on rare and unseen settings and exhibits strong generalization in standard evaluation settings. |
Task-Related In-Context Learning (2026.findings-acl)
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| Challenge: | Standard in-context learning assumes identical output spaces between test and retrieval datasets . however, in practice, these datasets can be fully aligned, partially alignes, or fully disjoint in label space . |
| Approach: | They propose a framework for in-context learning under output-space mismatch . they identify demonstrations relevant to the test label space via a Bayesian probabilistic criterion . |
| Outcome: | The proposed framework achieves state-of-the-art results across three LLMs, three task types, and four datasets. |