Papers with model-agnostic approach
Model Agnostic Answer Reranking System for Adversarial Question Answering (2021.eacl-srw)
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| Challenge: | Existing methods for adversarial QA are often model specific and require retraining of the model . authors propose a simple method that can be applied directly to any QA model based on a model-agnostic approach . |
| Approach: | They propose a model-agnostic approach that explicitly reranks candidate answers . they use a QA model that scores candidates on the basis of content overlap with the question . |
| Outcome: | The proposed method outperforms state-of-the-art models on adversarial examples without retraining. |
SLOT: Structuring the Output of Large Language Models (2025.emnlp-industry)
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| Challenge: | Structured outputs are essential for large language models (LLMs) but often deviate from predefined schemas hampering reliable application development. |
| Approach: | They propose a model-agnostic approach that transforms unstructured LLM outputs into precise structured formats. |
| Outcome: | The proposed model-agnostic approach transforms unstructured LLM outputs into precise structured formats. |
Consistency Training by Synthetic Question Generation for Conversational Question Answering (2024.acl-short)
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| Challenge: | Existing methods that use historical information to address user queries in conversational question-answering (CQA) contexts use the gold answers of history instead of the predicted ones. |
| Approach: | They propose a model-agnostic approach that augments historical information with synthetic questions and employs consistency training to implicitly make the reasoning robust to irrelevant history. |
| Outcome: | The proposed model improves in later turns of the conversation when dealing with questions with a large historical context. |
Explainable Hallucination through Natural Language Inference Mapping (2025.findings-acl)
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| Challenge: | Large language models (LLMs) often generate hallucinated content, making it crucial to identify and quantify inconsistencies in their outputs. |
| Approach: | They propose a framework that maps entailment and contradiction relations between inputs and outputs using a natural language inference model. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by five percentage points while providing clear, interpretable explanations. |
Model-Agnostic Bias Measurement in Link Prediction (2023.findings-eacl)
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| Challenge: | Existing work investigating social bias in factual knowledge graphs has focused on knowledge graph embeddings, so more recent classes of models achieving superior results by fine-tuning Transformers have not yet been investigated. |
| Approach: | They propose a model-agnostic approach for bias measurement leveraging fairness metrics to compare bias in knowledge graph embedding-based predictions (KG only) with models that use pre-trained, Transformer-based language models (KG+LM). |
| Outcome: | The proposed model-agnostic approach compares gender bias in occupation predictions with models that use pre-trained, Transformer-based language models (KG+LM). |
VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models (2024.acl-long)
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| Challenge: | Recent approaches in domain-specific named entity recognition (NER) have shown remarkable advances, but they still lack faithfulness, producing erroneous predictions. |
| Approach: | They propose a framework that revises errors from existing NER methods using knowledge to produce more faithful predictions. |
| Outcome: | The proposed framework can validate errors from existing models as a model-agnostic approach. |
Analyzing LLM Instruction Optimization for Tabular Fact Verification (2026.findings-eacl)
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Xiaotang Du, Giwon Hong, Wai-Chung Kwan, Rohit Saxena, Ivan Titov, Pasquale Minervini, Emily Allaway
| Challenge: | evaluating instruction optimization for tabular fact verification is a key challenge for reliable NLP systems. |
| Approach: | They compare instruction optimization for tabular fact verification with a framework based on DSPy . they find that instruction optimization consistently improves verification accuracy . |
| Outcome: | The proposed method improves verification accuracy across four benchmarks and three model families. |
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models struggle to meet user’s needs when required to generate responses of a specific length due to their inherent difficulty in accurately perceiving numerical constraints. |
| Approach: | They propose a Target Length Generation Task and propose RULER, a model-agnostic approach that controls generated length for large language models. |
| Outcome: | The proposed model-agnostic approach improves instruction-following ability of large language models under length-constrained instructions and can generate appropriate MLT when length constraints are not explicitly provided. |
KPDROP: Improving Absent Keyphrase Generation (2022.findings-emnlp)
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| Challenge: | Existing approaches to generate absent keyphrases have limited results in generating present keyphrase. |
| Approach: | They propose a model-agnostic approach to generate absent keyphrases by randomly dropping present keyphrase from a document and turning them into artificial absent key phrases during training. |
| Outcome: | The proposed approach improves absent keyphrase generation in both supervised and resource-constrained settings. |
Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System (2023.findings-acl)
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| Challenge: | Recent work on end-to-end dialogue models with pre-trained dialogue corpora shows promising performance in the conversational system. |
| Approach: | They propose an end-to-end TOD system with task-optimized adapters which learn independently per task adding only small number of parameters after fixed layers of pre-trained network. |
| Outcome: | The proposed system achieves state-of-the-art performance on the MultiWOZ benchmark compared to existing models. |
Opinions Are Not Always Positive: Debiasing Opinion Summarization with Model-Specific and Model-Agnostic Methods (2024.lrec-main)
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| Challenge: | Existing opinion summarization frameworks are reluctant to generate negative summaries given input of negative opinions. |
| Approach: | They propose to disentangle input into sentiment-relevant and sentiment-irrelevant components through adversarial loss. |
| Outcome: | The proposed approaches reduce sentiment bias in the existing opinion summarization dataset . the proposed approaches generate better summaries with a more balanced emotional polarity distribution . |
VIEWS: Entity-Aware News Video Captioning (2024.emnlp-main)
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Hammad Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang, Anurag Arnab, Feng Han, Yukun Zhu, Xuande Feng, Kevin Zhang, Jialu Liu, Shih-Fu Chang
| Challenge: | Existing video captioning benchmarks and models produce generic captions for videos that lack specific identification of individuals, locations, or organizations. |
| Approach: | They propose a task of directly summarizing news videos into captions that are entity-aware . they validate the effectiveness of their approach across three video captioning models . |
| Outcome: | The proposed approach is effective across three video captioning models. |
DcLM: Output Length Control of Large Language Models via Dynamic Length Markers (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have limited awareness of output length, making it difficult to satisfy precise length requirements. |
| Approach: | They propose a model-agnostic approach that introduces dynamic length markers to guide length-controllable outputs. |
| Outcome: | The proposed method significantly reduces length deviation across multiple datasets. |
Embedding-Free RAG (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) is the current state-of-the-art method for mitigating the shortcomings of large language models. |
| Approach: | They propose a model-agnostic approach to retrieval-augmented generation that leverages generalized reasoning abilities of large language models. |
| Outcome: | Embedding-free RAG outperforms existing state-of-the-art methods in a wide range of domains. |
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps (2025.findings-acl)
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| Challenge: | Existing approaches to augmented generation ignore the overlap in retrieval results . overlapping content is redundantly represented, affecting the overall efficiency. |
| Approach: | They propose a model-agnostic approach to re-augmented generation that speeds up prefilling and decoding . they propose an instruction-driven module to guide the model to more suitable ways for LLMs . |
| Outcome: | The proposed approach achieves 2.79 and 2.33 times significant acceleration on average for prefilling and decoding respectively while maintaining equal generation quality. |