3 citations · 7 across the 13 of their papers we have counts for
7 papers · 1 filter
PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models
Chengbing Wang, Wuqiang Zheng, Yang Zhang +5
Large Language Models (LLMs) are increasingly deployed in human-centric applications, yet they often fail to provide substantive emotional support. While Reinforcement Learning (RL…
REPAIR: Robust Editing via Progressive Adaptive Intervention and Reintegration
Yisu Wang, Ming Wang, Haoyuan Song +4
Post-training for large language models (LLMs) is constrained by the high cost of acquiring new knowledge or correcting errors and by the unintended side effects that frequently ar…
SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Dhruv Gupta, Gayathri Ganesh Lakshmy, Yiqing Xie
Retrieval-Augmented Code Generation (RACG) is a critical technique for enhancing code generation by retrieving relevant information. In this work, we conduct an in-depth analysis o…
RepoST: Scalable Repository-Level Coding Environment Construction with Sandbox Testing
Yiqing Xie, Alex Xie, Divyanshu Sheth +3
We present RepoST, a scalable method to construct environments that provide execution feedback for repository-level code generation for both training and evaluation. Unlike existin…
TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
Frank F. Xu, Yufan Song, Boxuan Li +18
We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the sa…
Improving Model Factuality with Fine-grained Critique-based Evaluator
Yiqing Xie, Wenxuan Zhou, Pradyot Prakash +9
Factuality evaluation aims to detect factual errors produced by language models (LMs) and hence guide the development of more factual models. Towards this goal, we train a factuali…
Data Augmentation for Code Translation with Comparable Corpora and Multiple References
Yiqing Xie, Atharva Naik, Daniel Fried +1
One major challenge of translating code between programming languages is that parallel training data is often limited. To overcome this challenge, we present two data augmentation…