Papers with iterative method
Self-Vocabularizing Training for Neural Machine Translation (2025.naacl-srw)
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| Challenge: | Past vocabulary learning techniques identify relevant vocabulary before training, relying on corpus statistics or frequency counts without considering contextual information or the model's ability to represent it. |
| Approach: | They propose a method that self-vocabularizes a smaller, more optimal vocabulary by pairing source sentences with the model's predictions to define a new vocabulary. |
| Outcome: | The proposed method produces a 1.49 BLEU improvement in the simulated model and an increase in unique token usage and a 6–8% reduction in vocabulary size. |
Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoning (2025.naacl-short)
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| Challenge: | Fact verification (FV) aims to assess the veracity of a claim based on relevant evidence. |
| Approach: | They propose to use iterative fact verification to assess the veracity of a claim based on relevant evidence. |
| Outcome: | The proposed system improves on three medical fact-checking datasets and evaluates with multiple settings including different LLMs, external web search, and structured reasoning using logic predicates. |
An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings (L18-1)
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| Challenge: | a study on the Algerian Arabic dialect aims to build a lexicon of words written in Arabic or Latin script . multilinguality of the corpus is due to the fact that people use several languages to post comments . stretched letters, misspelled words, emoticons, condensed writing are among the problems . |
| Approach: | They propose to build automatically from a social network an Algerian dialect lexicon. |
| Outcome: | The proposed method leads to a score of 73% on a test lexicon . the study is based on analyzing a lexical corpus of an Algerian dialect . |
GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers (2024.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance across various mathematical reasoning benchmarks. |
| Approach: | They introduce an adversarial grade school math dataset and explore whether LLMs can be more robust when questions are slightly changed. |
| Outcome: | The proposed method generates and verifies each intermediate thought based on its reasoning goal and calculation result. |
Learning Programmatic Idioms for Scalable Semantic Parsing (D19-1)
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| Challenge: | In state-of-the-art semantic parsers map natural language instructions to source code . idioms improve the accuracy of semantic parses, allowing for faster decoding . |
| Approach: | They propose an iterative method to extract code idioms from large source code corpora . they use most-frequent subtrees of their syntax trees to train semantic parsers to apply them . |
| Outcome: | The proposed method improves the state-of-the-art semantic parsers' accuracy and training time by more than 50%. |
Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with People (2024.acl-long)
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| Challenge: | Existing taxonomies or text corpora suffer from experimenter bias and are not representative of real-world distributions. |
| Approach: | They propose an iterative method for simultaneously eliciting conversational tones and sentences . they run 50 iterations with human participants and GPT-4 and obtain a dataset of sentences and frequent conversational tone. |
| Outcome: | The proposed method can be used to characterize the differences between humans and LLMs. |
ControlMath: Controllable Data Generation Promotes Math Generalist Models (2024.emnlp-main)
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| Challenge: | Currently, mathematical reasoning is one of the most challenging areas for closed-source LLMs. |
| Approach: | They propose an iterative method involving an equation-generator module and two LLM-based agents that generate diverse equations and transform them into math word problems. |
| Outcome: | The proposed method enables the generation of diverse math problems, not limited to specific domains or distributions. |