7 citations · 21 across the 18 of their papers we have counts for
12 papers · 1 filter
Deep Research as Rubric for Reinforcement Learning
Wangyi Mei, Zhouhong Gu, Zhenhan Bai +9
Open-ended reasoning and long-form generation tasks lack reliable automatic verification signals for reward-based policy optimization. Rubrics offer a promising alternative, but ex…
Skeletons Matter: Dynamic Data Augmentation for Text-to-Query
Yuchen Ji, Bo Xu, Jie Shi +5
The task of translating natural language questions into query languages has long been a central focus in semantic parsing. Recent advancements in Large Language Models (LLMs) have…
RLAP: A Reinforcement Learning Enhanced Adaptive Planning Framework for Multi-step NLP Task Solving
Zepeng Ding, Dixuan Wang, Ziqin Luo +3
Multi-step planning has been widely employed to enhance the performance of large language models (LLMs) on downstream natural language processing (NLP) tasks, which decomposes the…
BookWorld: From Novels to Interactive Agent Societies for Creative Story Generation
Yiting Ran, Xintao Wang, Tian Qiu +3
Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems. Prior efforts focus on agent societies created from scratch, assigning a…
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy
Guochao Jiang, Ziqin Luo, Chengwei Hu +2
Many previous models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), i.e., the tokens in the entity mentions of the test samples have not appeared…
QUILL: Quotation Generation Enhancement of Large Language Models
Jin Xiao, Bowei Zhang, Qianyu He +6
While Large language models (LLMs) have become excellent writing assistants, they still struggle with quotation generation. This is because they either hallucinate when providing f…
AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction
Yuchen Shi, Guochao Jiang, Tian Qiu +1
The relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence, leading to the…
P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models
Guochao Jiang, Zepeng Ding, Yuchen Shi +1
In recent years, the rise of large language models (LLMs) has made it possible to directly achieve named entity recognition (NER) without any demonstration samples or only using a…
Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data
Yiting Ran, Xintao Wang, Rui Xu +4
Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia.While existing RPAs…
Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction
Zepeng Ding, Ruiyang Ke, Wenhao Huang +4
Existing research on large language models (LLMs) shows that they can solve information extraction tasks through multi-step planning. However, their extraction behavior on complex…
SED: Self-Evaluation Decoding Enhances Large Language Models for Better Generation
Ziqin Luo, Haixia Han, Haokun Zhao +6
Existing Large Language Models (LLMs) generate text through unidirectional autoregressive decoding methods to respond to various user queries. These methods tend to consider token…
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
Dixuan Wang, Yanda Li, Junyuan Jiang +5
Large Language Models (LLMs) have shown remarkable capabilities in language understanding and generation. Nonetheless, it was also witnessed that LLMs tend to produce inaccurate re…