6 citations · 10 across the 5 of their papers we have counts for
5 papers
An Empirical Study on Influence-Based Pretraining Data Selection for Code Large Language Models
Chengli Xing, Zhengran Zeng, Gexiang Fang +3
Recent advancements in code large language models (Code-LLMs) have demonstrated remarkable capabilities in resolving programming related tasks. Meanwhile, researchers have recogniz…
ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement
Xiangyu Peng, Congying Xia, Xinyi Yang +3
Post-training Large Language Models (LLMs) with explicit reasoning trajectories can enhance their reasoning abilities. However, acquiring such high-quality trajectory data typicall…
FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability
Congying Xia, Chen Xing, Jiangshu Du +5
This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capabi…
Lemur: Harmonizing Natural Language and Code for Language Agents
Yiheng Xu, Hongjin Su, Chen Xing +13
We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agen…
Value Retrieval with Arbitrary Queries for Form-like Documents
Mingfei Gao, Le Xue, Chetan Ramaiah +3
We propose value retrieval with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of field…