The paper delivers a taxonomy of seven LLM study types in software engineering along with eight guidelines that separate mandatory requirements from recommended practices to address reproducibility challenges.
Adapting multilingual speech representation model for a new, underresourced language through multilingual fine-tuning and continued pretraining,
5 Pith papers cite this work, alongside 40 external citations. Polarity classification is still indexing.
5
Pith papers citing it
40
external citations · OpenAlex
representative citing papers
TravelEval is a new benchmark with a six-dimensional evaluation framework, realistic data sandbox, and simulation-based global assessment for LLM-powered travel planning agents.
An unsupervised character-level CNN encoder with attention-based RNN decoder, trained on Clueweb09 anchor phrases, generates query reformulations that improve retrieval on TREC collections.
Continual pre-training on Sinhala followed by Dhivehi fine-tuning achieves 12.89% WER, but KenLM decoding contributes 27+ absolute WER points, dwarfing the 0.61-point transfer gain.
Surveys energy footprints of image ML and proposes modest technical solutions including tiny models, low-precision hardware, and true-cost accounting driven by critiques of shareholder efficiency metrics.
citing papers explorer
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Guidelines for Empirical Studies in Software Engineering involving Large Language Models
The paper delivers a taxonomy of seven LLM study types in software engineering along with eight guidelines that separate mandatory requirements from recommended practices to address reproducibility challenges.
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TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents
TravelEval is a new benchmark with a six-dimensional evaluation framework, realistic data sandbox, and simulation-based global assessment for LLM-powered travel planning agents.
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Learning to Reformulate the Queries on the WEB
An unsupervised character-level CNN encoder with attention-based RNN decoder, trained on Clueweb09 anchor phrases, generates query reformulations that improve retrieval on TREC collections.
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From Sinhala to Dhivehi: Cross-Lingual Transfer Learning for Low-Resource Speech Recognition
Continual pre-training on Sinhala followed by Dhivehi fine-tuning achieves 12.89% WER, but KenLM decoding contributes 27+ absolute WER points, dwarfing the 0.61-point transfer gain.
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Modest, artistic, and radical solutions to the environmental impact of image-generating machine learning
Surveys energy footprints of image ML and proposes modest technical solutions including tiny models, low-precision hardware, and true-cost accounting driven by critiques of shareholder efficiency metrics.