Papers with Generator
Pre-training Language Model as a Multi-perspective Course Learner (2023.findings-acl)
Copied to clipboard
Beiduo Chen, Shaohan Huang, Zihan Zhang, Wu Guo, Zhenhua Ling, Haizhen Huang, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Experimental results show that our method significantly improves ELECTRA’s average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks. |
| Approach: | They propose a multi-perspective course learning method to fetch many degrees and visual angles for sample-efficient pre-training and to fully leverage the relationship between generator and discriminator. |
| Outcome: | The proposed method improves ELECTRA's performance on GLUE and SQuAD 2.0 benchmarks and overshadows recent advanced ELECL-style models under the same settings. |
BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems (2022.emnlp-demos)
Copied to clipboard
| Challenge: | evaluating and troubleshooting production TOD systems is still a largely manual process requiring large amount of human conversations with the systems. |
| Approach: | They propose a data-efficient end-to-end Bot SIMulation framework for commercial task-oriented dialog systems that can generate user queries and generate semantic-level dialog acts and entities from bot definitions. |
| Outcome: | The proposed framework is able to infer semantic-level dialog acts and entities from bot definitions and generate user queries via model-based paraphrasing. |
Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to disfluency detection heavily depend on labeled data. |
| Approach: | They propose a Planner-Generator based disfluency generation model that generates natural disfluent texts as augmented data. |
| Outcome: | The proposed model outperforms baselines and leads to state-of-the-art performance on Switchboard corpus. |
Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation (2022.naacl-main)
Copied to clipboard
| Challenge: | omitted tokens from the context contribute to incomplete utterance restoration (IUR) understanding conversational interactions through NLP has become important with increasing connectivity and range of capabilities. |
| Approach: | They propose a model for incomplete utterance restoration called JET . they construct a Picker that identifies omitted tokens and two label creation methods to support the picker. |
| Outcome: | The proposed model is better than pretrained T5 and non-generative language model methods on four benchmark datasets in extraction and abstraction scenarios. |
SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graph (2024.acl-long)
Copied to clipboard
| Challenge: | Existing KG construction methods rely on human intervention to attain qualified KGs, which severely hinders the practical application of domain KG. |
| Approach: | They propose a general KG construction framework that uses large language models as "S**killed" A**utomatic C**onstructors for domain knowledge (G**raph) |
| Outcome: | The proposed framework generates specialized multi-level knowledge graphs at the scale of over one million nodes and achieves 89.32% precision rate compared to state-of-the-art methods. |
FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to automate scientific research are limited by human cognitive constraints and timeintensive workflows. |
| Approach: | They propose a framework that enhances medical paper generation through iterative refinement and structured feedback. |
| Outcome: | The proposed framework achieves significant improvements over conventional methods across multiple models and evaluation dimensions. |
A Strategic Coordination Framework of Small LMs Matches Large LMs in Data Synthesis (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models suffer from high computational costs and environmental inefficiency . smaller LMs are more accessible and sustainable, but their individual capabilities often fall short . a collaborative framework for small LM combines specialized roles to iterative refinement and quality control . |
| Approach: | They propose a framework that aggregates specialized roles across small LMs to iterative refinement and quality control typically achieved by a single large LM. |
| Outcome: | The proposed framework aggregates specialized roles across small LMs to iterative refinement and quality control typically achieved by large LM. |
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) due to noisy and fabricating content, it is inevitable that RAG systems are vulnerable to these noises and prone to respond incorrectly. |
| Approach: | They propose to optimize retrieval-augmented generation (RGG) with an Adversarial Tuning Multi-agent system (ATM) ATM steers the Generator to have a robust perspective of useful documents for question answering with the help of an auxiliary Attacker agent. |
| Outcome: | The proposed system improves the retrieval-augmented generator with an auxiliary Attacker agent and can discriminate useful documents amongst fabrications. |
Automatic Transmission for LLM Tiers: Optimizing Cost and Accuracy in Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are powerful tools for a wide range of natural language tasks. |
| Approach: | They propose an LLM automatic transmission framework that automatically selects LLM tiers without training. |
| Outcome: | The proposed framework achieves superior performance while reducing costs. |
Faithful Persona-based Conversational Dataset Generation with Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for training conversational AI models do not sufficiently model their users. |
| Approach: | They propose a generator-critic architecture framework to expand the initial dataset while improving the quality of its conversations. |
| Outcome: | The proposed framework expands the initial dataset while improving the quality of its conversations. |
From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent (2026.acl-long)
Copied to clipboard
Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu
| Challenge: | Large language models produce content lacking pedagogical depth when asked to generate lessons . |
| Approach: | They propose a framework that allows teachers to select content according to pedagogical intent and sequence topics so foundations precede applications. |
| Outcome: | The framework achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines. |
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing paradigms of Retrieval-Augmented Generation (RAG) are suboptimal due to exposure bias, a mismatch between pre-training data distribution and retrieved information. |
| Approach: | They propose to bridge retrieval results and the LLM’s reasoning ability through Verbal Annotations, analytic narratives that explicitly articulate the logical connection between a search query and retrieved contexts. |
| Outcome: | The proposed framework achieves state-of-the-art performance on complex Question Answering benchmarks validating the effectiveness of the proposed framework. |