mlsanityData to 7 Oct 2026

CALICO: Conversational Agent Localization via Synthetic Data Generation

arXiv 2412.05388 ↗First posted 6 Dec 2024cs.CL cs.AI cs.LGcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

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Abstract (arXiv)

We present CALICO, a method to fine-tune Large Language Models (LLMs) to localize conversational agent training data from one language to another. For slots (named entities), CALICO supports three operations: verbatim copy, literal translation, and localization, i.e. generating slot values more appropriate in the target language, such as city and airport names located in countries where the language is spoken. Furthermore, we design an iterative filtering mechanism to discard noisy generated samples, which we show boosts the performance of the downstream conversational agent. To prove the effectiveness of CALICO, we build and release a new human-localized (HL) version of the MultiATIS++ travel information test set in 8 languages. Compared to the original human-translated (HT) version of the test set, we show that our new HL version is more challenging. We also show that CALICO out-performs state-of-the-art LINGUIST (which relies on literal slot translation out of context) both on the HT case, where CALICO generates more accurate slot translations, and on the HL case, where CALICO generates localized slots which are closer to the HL test set.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 19 of its 30 references that are in the map. First posted on arXiv 6 Dec 2024.

Builds on

19 of 30 references.

References · 19

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  2. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  3. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  4. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  5. 2019BERT for Joint Intent Classification and Slot Fillingcited by 195
  6. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  7. 2022MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languagescited by 142
  8. 2020End-to-End Slot Alignment and Recognition for Cross-Lingual NLUcited by 82
  9. 2020An Analysis of Simple Data Augmentation for Named Entity Recognitioncited by 75
  10. 2023PLACES: Prompting Language Models for Social Conversation Synthesiscited by 72
  11. 2022AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Modelcited by 57
  12. 2020SeqMix: Augmenting Active Sequence Labeling via Sequence Mixupcited by 52
7 more
  1. 2022Weakly Supervised Data Augmentation Through Prompting for Dialogue Understandingcited by 32
  2. 2022LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Taggingcited by 29
  3. 2018Efficient keyword spotting using dilated convolutions and gatingcited by 21
  4. 2022CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsingcited by 21
  5. 2023Exploring Zero and Few-shot Techniques for Intent Classificationcited by 21
  6. 2019Controlled Text Generation for Data Augmentation in Intelligent Artificial Agentscited by 20
  7. 2022Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systemscited by 15

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  3. 2026BabelArena: A Large-Scale Multilingual Benchmark for LLM Agents
  4. 2026GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling
  5. 2026GAIA-v2-LILT: Multilingual Adaptation of Agent Benchmark beyond Translation
  6. 2025Thinking While Speaking: Inference-Time Knowledge Transfer for Responsive and Intelligent Conversational Voice Agents
  7. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study
  8. 2026OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents