Search Results for author:
Found 17 papers, 12 papers with code
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
Nevertheless, we posit that LLMs inherently harbor role-play capabilities, owing to the extensive knowledge of characters and potential dialogues ingrained in their vast training corpora.
Speculative Contrastive Decoding
Large language models~(LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-optimal due to the exposure bias.
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
Zooter shows computation efficiency in inference as it introduces only a minor computation overhead of a routing function compared with reward model ranking methods.
Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning
The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets, as the model selects new data points most distinct from any existing ones according to its current embedding space.
Query and Response Augmentation Cannot Help Out-of-domain Math Reasoning Generalization
In this paper, we conduct an investigation for such data augmentation in math reasoning and are intended to answer: (1) What strategies of data augmentation are more effective; (2) What is the scaling relationship between the amount of augmented data and model performance; and (3) Can data augmentation incentivize generalization to out-of-domain mathematical reasoning tasks?
Ranked #50 on
Math Word Problem Solving
on MATH
(using extra training data)
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
We propose four intriguing research questions to explore the association between model performance and various factors including data amount, composition ratio, model size and SFT strategies.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Based on this observation, we propose a data selector based on InsTag to select 6K diverse and complex samples from open-source datasets and fine-tune models on InsTag-selected data.
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
We find with augmented samples containing more distinct reasoning paths, RFT improves mathematical reasoning performance more for LLMs.
Ranked #100 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
PIVOINE: Instruction Tuning for Open-world Information Extraction
In particular, we construct INSTRUCTOPENWIKI, a substantial instruction tuning dataset for Open-world IE enriched with a comprehensive corpus, extensive annotations, and diverse instructions.
Exploring Partial Knowledge Base Inference in Biomedical Entity Linking
Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED).
Multi-hop Evidence Retrieval for Cross-document Relation Extraction
Relation Extraction (RE) has been extended to cross-document scenarios because many relations are not simply described in a single document.
Knowledge-Enhanced Relation Extraction Dataset
Recently, knowledge-enhanced methods leveraging auxiliary knowledge graphs have emerged in relation extraction, surpassing traditional text-based approaches.
Summarization as Indirect Supervision for Relation Extraction
Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i. e., extracting a kind of synoptical information that describes the relation of entity mentions.
Ranked #6 on
Relation Extraction
on TACRED
BIOS: An Algorithmically Generated Biomedical Knowledge Graph
For decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with today's AI development, and a transition to algorithmically generated BioMedKGs is necessary.
Multimodal Learning on Graphs for Disease Relation Extraction
On a dataset annotated by human experts, REMAP improves text-based disease relation extraction by 10. 0% (accuracy) and 17. 2% (F1-score) by fusing disease knowledge graphs with text information.
High-throughput relation extraction algorithm development associating knowledge articles and electronic health records
In this paper, we present Hi-RES, a framework for high-throughput relation extraction algorithm development.
