Robust ASR Error Correction with Conservative Data Filtering (2024.emnlp-industry)
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| Challenge: | Error correction (EC) based on large language models is an emerging technology to enhance the performance of automatic speech recognition systems. |
| Approach: | They propose to pair large set of ASR hypotheses with gold references to improve linguistic acceptability over sources and be inferable from available context. |
| Outcome: | The proposed approach significantly reduces overcorrection and improves quality in out-of-domain (OOD) settings. |
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ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction (2025.emnlp-industry)
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| Challenge: | Automatic Speech Recognition (ASR) systems have a substantial number of erroneous recognition due to environmental noise, ambiguity, etc. |
| Approach: | They use a benchmark dataset to analyze ASR errors in the Chinese language . they then apply large language models to correct ASR error correction . |
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Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR). |
| Approach: | They propose a multimodal LLM to receive source speech as extra input and reformat it as a cloze test with logits calibration to remove input information redundancy and simplify GER with clear instructions. |
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Harnessing Large Language Models as Post-hoc Correctors (2024.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their effectiveness in a wide range of tasks, including machine translation and commonsense reasoning. |
| Approach: | They propose a training-free framework that can work as a post-hoc corrector to propose corrections for ML models. |
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Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection (2026.findings-eacl)
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| Challenge: | Existing methods to improve robustness require changing the fine-tuning process or large-scale data augmentation, which are infeasible or cost prohibitive for closed-source models. |
| Approach: | They propose to prioritize more complex examples or replace existing training examples with LLM-generated data to improve performance on OOD NLI datasets. |
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An (unhelpful) guide to selecting the best ASR architecture for your under-resourced language (2023.acl-short)
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| Challenge: | English ASR now has word error rates comparable to that of human transcriptionists, but only for the handful of the world's 7000 languages with abundant training resources. |
| Approach: | They propose to use four of the most popular ASR toolkits to train ASR models for eleven languages with limited ASR training resources: eleven widely spoken languages of Africa, Asia, and South America, one endangered language of Central America, and three critically endangered languages of North America. |
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Post-ASR Correction in Hindi: Comparing Language Models and Large Language Models in Low-Resource Scenarios (2026.eacl-short)
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| Challenge: | Automatic Speech Recognition (ASR) systems for low-resource languages produce erroneous transcripts due to limited annotated data and linguistic complexity. |
| Approach: | They compare language models and large language models for post-ASR correction in Hindi . they observe a scaling trend under zero-shot ICL where mid-sized LLMs degrade performance before marginal recovery at extreme scales. |
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Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation (2025.findings-acl)
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Sreyan Ghosh, Mohammad Sadegh Rasooli, Michael Levit, Peidong Wang, Jian Xue, Dinesh Manocha, Jinyu Li
| Challenge: | Generative Error Correction (GEC) is a powerful post-processing method to boost the performance of Automatic Speech Recognition systems. |
| Approach: | They propose a method to augment GEC models with retrieved entities to improve accuracy in out-of-domain and out-od scenarios. |
| Outcome: | The proposed method outperforms baseline models on multiple datasets and settings. |
GSM-Noise: Exploring and Enhancing Large Language Models’ Reasoning under Noisy Inputs (2026.findings-acl)
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| Challenge: | Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust . |
| Approach: | They propose to use GSM-Noise to refine inputs before engaging in in-depth analysis to improve LLM robustness under noisy conditions. |
| Outcome: | The proposed model can achieve consistent performance gains under noisy conditions with prompt engineering, supervised finetuning, and reinforcement learning. |
Alirector: Alignment-Enhanced Chinese Grammatical Error Corrector (2024.findings-acl)
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| Challenge: | Existing methods to address overcorrection in Chinese grammatical error correction (CGEC) are difficult to adapt to decoder-only large language models (LLMs). |
| Approach: | They propose an alignment-enhanced corrector for the overcorrection problem that applies to both Seq2Seq models and decoder-only large language models (LLMs). |
| Outcome: | The proposed corrector alleviates the overcorrection problem in Chinese grammatical error correction (CGEC) using generative models and decoder-only large language models. |
A Simple Recipe for Multilingual Grammatical Error Correction (2021.acl-short)
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| Challenge: | Modern approaches view the task of Grammatical Error Correction (GEC) as monolingual text-to-text rewriting and employ encoderdecoder neural architectures. |
| Approach: | They propose a language-agnostic method to generate a large number of synthetic examples and use large-scale multilingual language models to train state-of-the-art GEC models. |
| Outcome: | The proposed method surpasses state-of-the-art results on GEC benchmarks in English, Czech, German and Russian. |