47 citations · 170 across the 17 of their papers we have counts for
18 papers
MSQA: Benchmarking LLMs on Graduate-Level Materials Science Reasoning and Knowledge
Jerry Junyang Cheung, Shiyao Shen, Yuchen Zhuang +3
Despite recent advances in large language models (LLMs) for materials science, there is a lack of benchmarks for evaluating their domain-specific knowledge and complex reasoning ab…
MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering
Rushi Qiang, Yuchen Zhuang, Yinghao Li +8
We introduce MLE-Dojo, a Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents in iterative machine…
Language Model Uncertainty Quantification with Attention Chain
Yinghao Li, Rushi Qiang, Lama Moukheiber +1
Accurately quantifying a large language model's (LLM) predictive uncertainty is crucial for judging the reliability of its answers. While most existing research focuses on short, d…
Ensembles of Low-Rank Expert Adapters
Yinghao Li, Vianne Gao, Chao Zhang +1
The training and fine-tuning of large language models (LLMs) often involve diverse textual data from multiple sources, which poses challenges due to conflicting gradient directions…
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models
Yuzhao Heng, Chunyuan Deng, Yitong Li +4
Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity re…
A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction
Yinghao Li, Rampi Ramprasad, Chao Zhang
Large language models (LLMs) have demonstrated impressive abilities in generating unstructured natural language according to instructions. However, their performance can be inconsi…
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
Haorui Wang, Rongzhi Zhang, Yinghao Li +4
Large Language Models (LLMs) have recently showcased remarkable reasoning abilities. However, larger models often surpass their smaller counterparts in reasoning tasks, posing the…
PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature
Jerry Junyang Cheung, Yuchen Zhuang, Yinghao Li +5
Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever. However, there are no e…
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
Yinghao Li, Haorui Wang, Chao Zhang
Large Language Models (LLMs) have shown remarkable proficiency in language understanding and have been successfully applied to a variety of real-world tasks through task-specific f…
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
Yinghao Li, Lingkai Kong, Yuanqi Du +4
Large molecular representation models pre-trained on massive unlabeled data have shown great success in predicting molecular properties. However, these models may tend to overfit t…
Extracting Shopping Interest-Related Product Types from the Web
Yinghao Li, Colin Lockard, Prashant Shiralkar +1
Recommending a diversity of product types (PTs) is important for a good shopping experience when customers are looking for products around their high-level shopping interests (SIs)…
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
Yuchen Zhuang, Yinghao Li, Jerry Junyang Cheung +5
We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and mod…
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
Yinghao Li, Le Song, Chao Zhang
Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually anno…
WRENCH: A Comprehensive Benchmark for Weak Supervision
Jieyu Zhang, Yue Yu, Yinghao Li +4
Recent Weak Supervision (WS) approaches have had widespread success in easing the bottleneck of labeling training data for machine learning by synthesizing labels from multiple pot…
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
Yinghao Li, Pranav Shetty, Lucas Liu +2
We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak sup…
Denoising Multi-Source Weak Supervision for Neural Text Classification
Wendi Ren, Yinghao Li, Hanting Su +3
We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challe…
Transformer-Based Neural Text Generation with Syntactic Guidance
Yinghao Li, Rui Feng, Isaac Rehg +1
We study the problem of using (partial) constituency parse trees as syntactic guidance for controlled text generation. Existing approaches to this problem use recurrent structures,…
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
Yue Yu, Yinghao Li, Jiaming Shen +3
Taxonomies are important knowledge ontologies that underpin numerous applications on a daily basis, but many taxonomies used in practice suffer from the low coverage issue. We stud…