mlsanityData to 7 Oct 2026

AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization

arXiv 2407.11591 ↗First posted 16 Jul 2024cs.CLcited by 9

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Abstract (arXiv)

Despite the advances in the abstractive summarization task using Large Language Models (LLM), there is a lack of research that asses their abilities to easily adapt to different domains. We evaluate the domain adaptation abilities of a wide range of LLMs on the summarization task across various domains in both fine-tuning and in-context learning settings. We also present AdaptEval, the first domain adaptation evaluation suite. AdaptEval includes a domain benchmark and a set of metrics to facilitate the analysis of domain adaptation. Our results demonstrate that LLMs exhibit comparable performance in the in-context learning setting, regardless of their parameter scale.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

17 of 29 references.

References · 17

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  4. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  5. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  6. 2023LIMA: Less Is More for Alignmentcited by 1,080
  7. 2023The Falcon Series of Open Language Modelscited by 526
  8. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 477
  9. 2023Benchmarking Large Language Models for News Summarizationcited by 444
  10. 2021Efficient Attentions for Long Document Summarizationcited by 342
  11. 2022News Summarization and Evaluation in the Era of GPT-3cited by 324
  12. 2023The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learningcited by 251
5 more
  1. 2022SCROLLS: Standardized CompaRison Over Long Language Sequencescited by 170
  2. 2021AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarizationcited by 51
  3. 2022Investigating Efficiently Extending Transformers for Long Input Summarizationcited by 43
  4. 2023Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Modelscited by 35
  5. 2024Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?cited by 32

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 2.

9 papers.

  1. 2025AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptationcited by 15
  2. 2024DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarizationcited by 4
  3. 2024Evaluating Large Language Models on Financial Report Summarization: An Empirical Studycited by 3
  4. 2025Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guaranteescited by 3
  5. 2025Natural Language Tools: A Natural Language Approach to Tool Calling In Large Language Agentscited by 3
  6. 2025DACP: Domain-Adaptive Continual Pre-Training of Large Language Models for Phone Conversation Summarizationcited by 2
  7. 2026An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shiftcited by 2
  8. 2025Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarizationcited by 0
  9. 2026Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptationcited by 0