Papers by Alexandru Coca
ASPERA: A Simulated Environment to Evaluate Planning for Complex Action Execution (2025.acl-long)
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Alexandru Coca, Mark Gaynor, Zhenxing Zhang, Jianpeng Cheng, Bo-Hsiang Tseng, Peter Boothroyd, Hector Martinez Alonso, Diarmuid O Seaghdha, Anders Johannsen
| Challenge: | ASPERA framework allows developers to guide LLM generation of high-quality tasks based on user queries, simulation state and corresponding validation programs. |
| Approach: | They develop a framework comprising an assistant library simulation and a human-assisted LLM data generation engine to guide LLM generation of high-quality tasks . they use a dataset to evaluate 250 tasks generated using ASPERA . |
| Outcome: | The proposed framework can guide LLM generation of high-quality tasks tackling data availability and evaluation robustness challenges. |
More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking (2023.findings-eacl)
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| Challenge: | Pre-trained language models (PLMs) can track only slots drawn from a database or domain ontology. |
| Approach: | They propose a framework for generating synthetic schemas which uses tree-based ranking to optimise lexical diversity and semantic faithfulness. |
| Outcome: | The proposed framework improves the generalisation of strong baselines by augmenting training data with prompts generated by the framework. |
Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries (2024.naacl-long)
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| Challenge: | Recent studies use in-context learning with large language models (LLM) to find similar dialogue exemplars for prompt learning. |
| Approach: | They propose to use a conversation retriever to find similar in-context examples for prompt learning. |
| Outcome: | The proposed approach improves on multiWOZ datasets with GPT-Neo-2.7B and LLaMA-7B/30B . |
uFACT: Unfaithful Alien-Corpora Training for Semantically Consistent Data-to-Text Generation (2022.findings-acl)
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| Challenge: | uFACT (Un-Faithful Alien Corpora Training) is a training corpus construction method for data-to-text generation models. |
| Approach: | They propose a training corpus construction method for data-to-text (d2t) generation models which augments a target corpus with alien corpora which have different semantic representations. |
| Outcome: | The proposed method generates utterances which represent the semantic content of the data sources more accurately compared to models trained on the target corpus alone. |
LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues (2024.naacl-srw)
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Joe Stacey, Jianpeng Cheng, John Torr, Tristan Guigue, Joris Driesen, Alexandru Coca, Mark Gaynor, Anders Johannsen
| Challenge: | Existing datasets with limited domain coverage and few challenging conversational phenomena are often unlabelled . Existing data is limited in quality and lacks a robust evaluation process . |
| Approach: | They propose a high quality data generation system that generates high quality dialogues using 4,277 conversations across 100 intents. |
| Outcome: | The proposed system produces high quality dialogue data with high quality labels. |