7 citations · 9 across the 11 of their papers we have counts for
11 papers
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Recent Advances in Large Langauge Model Benchmarks against Data Contamination: From Static to Dynamic Evaluation
Simin Chen, Yiming Chen, Zexin Li +8
Data contamination has received increasing attention in the era of large language models (LLMs) due to their reliance on vast Internet-derived training corpora. To mitigate the ris…
Unveiling the Achilles' Heel of NLG Evaluators: A Unified Adversarial Framework Driven by Large Language Models
Yiming Chen, Chen Zhang, Danqing Luo +3
The automatic evaluation of natural language generation (NLG) systems presents a long-lasting challenge. Recent studies have highlighted various neural metrics that align well with…
A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators
Chen Zhang, Luis Fernando D'Haro, Yiming Chen +2
Automatic evaluation is an integral aspect of dialogue system research. The traditional reference-based NLG metrics are generally found to be unsuitable for dialogue assessment. Co…
Dynamic Transformers Provide a False Sense of Efficiency
Yiming Chen, Simin Chen, Zexin Li +4
Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference. Multi-exit is a mainstream appr…
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation
Danqing Luo, Chen Zhang, Jiahui Xu +4
Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptat…
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
Yiming Chen, Yan Zhang, Bin Wang +2
Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the performanc…
Analyzing and Evaluating Faithfulness in Dialogue Summarization
Bin Wang, Chen Zhang, Yan Zhang +2
Dialogue summarization is abstractive in nature, making it suffer from factual errors. The factual correctness of summaries has the highest priority before practical applications.…
Investigating the Impact of Pre-trained Language Models on Dialog Evaluation
Chen Zhang, Luis Fernando D'Haro, Yiming Chen +2
Recently, there is a surge of interest in applying pre-trained language models (Pr-LM) in automatic open-domain dialog evaluation. Pr-LMs offer a promising direction for addressing…
Revisiting Self-Training for Few-Shot Learning of Language Model
Yiming Chen, Yan Zhang, Chen Zhang +3
As unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In…
DynaEval: Unifying Turn and Dialogue Level Evaluation
Chen Zhang, Yiming Chen, Luis Fernando D'Haro +4
A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are…