Papers with two-step process
QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input (2022.acl-demo)
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| Challenge: | Existing systems based on attributes or keywords render lyrics generation very limited . previous studies focused on generating lyrics based only on attributes and keywords . |
| Approach: | They propose to use Chinese passage-level text as input for lyrics generation . they initialize parameters with custom pretrained Chinese GPT-2 model and adopt a two-step process to fine-tune the model for better alignment between passage- level text and lyrics. |
| Outcome: | The proposed system is conditioned on passage-level text rather than attributes or keywords, rendering limited control over the content of the lyrics. |
Understanding Transformer Memorization Recall Through Idioms (2023.eacl-main)
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| Challenge: | Existing methods for analyzing memorization use definitions that are based on model performance, which changes between models and often also between training runs. |
| Approach: | They propose idioms as inputs that typically trigger memory recall and propose a set of English idiomas to test their methodological framework for probing and characterizing recall of memorized sequences in transformer LMs. |
| Outcome: | The proposed framework compares model behavior on memorized vs. non-memorized inputs across different model sizes and architectures. |
SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification (2026.acl-long)
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Tianyang Zhou, Ziyi Zhang, Haowen Lin, Somesh Jha, Mihai Christodorescu, Kirill Levchenko, Varun Chandrasekaran
| Challenge: | Large language models (LLMs) have shown promise in producing idiomatic translations, but offer no correctness guarantees. |
| Approach: | They propose a C-to-Rust translation tool that uses an initial "unidiomatic" translation followed by an "idiomatic refinement" they evaluate SACTOR on 200 programs from two datasets and two more complex scenarios . |
| Outcome: | The proposed tool delivers high end-to-end correctness and produces safe, idiomatic Rust with up to 7 fewer Clippy warnings. |
CEREC: A Corpus for Entity Resolution in Email Conversations (2020.coling-main)
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| Challenge: | e-mail corpus for entity resolution in email conversations is first large scale annotated corpus . ecc is a two-step process with minimal manual effort. |
| Approach: | They present the first large scale corpus for entity resolution in email conversations . they use 6001 email threads and 38,996 entity coreference chains to construct the corpus . |
| Outcome: | The proposed corpus is the first large scale annotated corpus for entity resolution in email conversations. |
UDAPTER - Efficient Domain Adaptation Using Adapters (2023.eacl-main)
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| Challenge: | Using adapters, unsupervised domain adaptation (UDA) is more parameter efficient and requires large-scale data to be effective. |
| Approach: | They propose to add small bottleneck layers to each layer of a pre-trained language model to make it more parameter efficient by adding adapters. |
| Outcome: | The proposed methods outperform unsupervised domain adaptation methods such as DANN and DSN in natural language inference and sentiment classification tasks. |
HEISIR: Hierarchical Expansion of Inverted Semantic Indexing for Training-free Retrieval of Conversational Data using LLMs (2025.findings-naacl)
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| Challenge: | Existing methods for retrieving dialogue data often face challenges in capturing semantic intent or require extensive labeling and fine-tuning. |
| Approach: | They propose a framework that enhances semantic understanding in conversational data retrieval by optimizing data ingestion. |
| Outcome: | The proposed framework outperforms fine-tuned models in retrieval and intent analysis. |
CROP: Contextual Region-Oriented Visual Token Pruning (2025.emnlp-main)
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| Challenge: | Existing VLMs process entire images, leading to excessive visual tokens . redundant image information also introduces a large number of visual token, requiring much higher memory and computation in VLM. |
| Approach: | They propose a framework to prune visual tokens using localization and pruning . they propose CROP to locate local image regions relevant to the query . |
| Outcome: | The proposed framework outperforms existing visual token pruning methods on a wide range of tasks. |
Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification (2022.lrec-1)
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| Challenge: | Existing work on question answering systems assumes all questions are intelligible and unambiguous . however, available datasets do not meet requirements for building commercial virtual assistants . |
| Approach: | They propose to make question answering systems more robust by classifying if question is intelligible and returning a clarification question for contextual ambiguity. |
| Outcome: | The proposed system can classify if the input question is intelligible and return a clarification question for ambiguous questions. |
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification (D19-1)
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| Challenge: | Existing methods for unsupervised sentiment modification lack specific information in text generated without parallel data . specificity-driven cascading approach can improve specificity of generated text and content preservation . |
| Approach: | They propose a specificity-driven cascading approach for unsupervised sentiment modification . the method performs target sentiment addition and content reconstruction independently . |
| Outcome: | The proposed method outperforms competitive systems by a large margin on Yelp and Amazon datasets. |
Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)
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| Challenge: | Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. |
| Approach: | They propose a fine-tuned model that integrates entity recognition and disambiguation in a unified framework. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain datasets and compares with baselines. |
A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks (2026.acl-long)
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Shuzheng Si, Haozhe Zhao, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, Maosong Sun
| Challenge: | Recent advances in large language models (LLMs) have leapt from static chatbots to versatile agents that tackle complex tasks such as science experiments. |
| Approach: | They propose a plan-and-execute framework and propose 'EAGLET' to enhance the executor agent's planning abilities without human effort. |
| Outcome: | The proposed method outperforms existing methods on three long-horizon tasks and reduces training costs by 8 compared to baselines. |
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025.coling-main)
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Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current Large Language Models struggle with complex entity taxonomies in open domains and lack NER capabilities. |
| Approach: | They propose a dataset to guide LLMs' generalization in Open NER under a universal entity taxonomy. |
| Outcome: | The proposed model outperforms GPT-4 in 3 out-of-domain benchmarks across 15 datasets and 6 languages. |
Compositional Generalization without Trees using Multiset Tagging and Latent Permutations (2023.acl-long)
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| Challenge: | Seq2seq models struggle with compositional generalization in semantic parsing, i.e. generalizing to unseen compositions or deeper recursion of phenomena that the model handles correctly in isolation. |
| Approach: | They propose a new way of parameterizing and predicting permutations by combining input tokens with multisets of output tokens and a method to backpropagate through the solver. |
| Outcome: | The proposed model outperforms pretrained models and prior work on realistic semantic parsing tasks that require generalization to longer examples. |
Is It Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models (2024.lrec-main)
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| Challenge: | Text simplification and elaboration tasks are limited to only relatively altering the readability of texts to cater to a diverse audience. |
| Approach: | They propose to generate 8 versions of a text at different readability levels using ChatGPT and Llama-2 and introduce a two-step process to generate paraphrases. |
| Outcome: | The proposed task requires the generation of 8 versions at various target readability levels for each input text. |
Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation (2025.findings-emnlp)
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Xing Zhang, Jiaheng Wen, Fangkai Yang, Yu Kang, Pu Zhao, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing code translation benchmarks focus on individual functions, overlooking repository-level challenges like intermodule coherence and dependency management. |
| Approach: | They propose a framework for benchmarking Java-to-C# translation at the repository level . it uses a translation framework guided by skeletons and fine-grained quality evaluation . |
| Outcome: | The proposed framework improves Java-to-C# translation quality at the repository level. |
DocCGen: Document-based Controlled Code Generation (2024.emnlp-main)
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Sameer Pimparkhede, Mehant Kammakomati, Srikanth Tamilselvam, Prince Kumar, Ashok Kumar, Pushpak Bhattacharyya
| Challenge: | Large language models (LLMs) produce state-of-the-art performance on natural language to code generation for resource-rich general-purpose languages like C++, Java, and Python. |
| Approach: | They propose a framework that breaks the NL-to-Code generation task into two steps . they use library documentation to detect the correct libraries and schema rules extracted from the documentation to constrain the decoding . |
| Outcome: | The proposed framework improves different sized language models across all six evaluation metrics, reducing syntactic and semantic errors in structured code. |
PPORTAL_ner: An Annotated Corpus of Portuguese Literary Entities (2024.lrec-main)
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| Challenge: | Annotated corpus of 25 literary texts provides a rich set of annotations for Named Entity Recognition models. |
| Approach: | They propose an annotation dataset that simplifies the development of Named Entity Recognition models for Portuguese literary texts. |
| Outcome: | The proposed dataset simplifies the development of Named Entity Recognition models for Portuguese literary works. |
Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning (2025.findings-acl)
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| Challenge: | Existing benchmarks for Theory of Mind (ToM) focus on whether agents have correct beliefs about others. |
| Approach: | They propose to evaluate Theory of Mind (ToM) capabilities in Large Language Models (LLMs) they propose to use the theory of mind to determine whether and how to invoke ToM . |
| Outcome: | The proposed frameworks can be used to evaluate the performance of large language models (LLMs) in biological agents. |