mlsanityData to 14 Oct 2026

Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognition

arXiv 2503.20588 ↗First posted 26 Mar 2025cs.CLcited by 1

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

Implicit discourse relation recognition (IDRR) -- the task of identifying the implicit coherence relation between two text spans -- requires deep semantic understanding. Recent studies have shown that zero- or few-shot approaches significantly lag behind supervised models, but LLMs may be useful for synthetic data augmentation, where LLMs generate a second argument following a specified coherence relation. We applied this approach in a cross-domain setting, generating discourse continuations using unlabelled target-domain data to adapt a base model which was trained on source-domain labelled data. Evaluations conducted on a large-scale test set revealed that different variations of the approach did not result in any significant improvements. We conclude that LLMs often fail to generate useful samples for IDRR, and emphasize the importance of considering both statistical significance and comparability when evaluating IDRR models.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 30 of its 71 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

30 of 71 references.

References · 30

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18 more
  1. 2020Training Question Answering Models From Synthetic Datacited by 95
  2. 2023GPTEval: A Survey on Assessments of ChatGPT and GPT-4cited by 73
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  10. 2023Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Designcited by 14
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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Discursive Circuits: How Do Language Models Understand Discourse Relations?cited by 4

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  3. 2025BeDiscovER: The Benchmark of Discourse Understanding in the Era of Reasoning Language Models
  4. 2024How to Understand "Support"? An Implicit-enhanced Causal Inference Approach for Weakly-supervised Phrase Grounding
  5. 2023How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning
  6. 2025CORDIAL: Can Multimodal Large Language Models Effectively Understand Coherence Relationships?
  7. 2023Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing
  8. 2024A synthetic data approach for domain generalization of NLI models