2 citations · 3 across the 11 of their papers we have counts for
12 papers
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment
Jing Zhao, Ting Zhen, Junwei Bao +2
Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcit…
Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts
Chen Yang, Guangyue Peng, Jiaying Zhu +12
We present Nanbeige4.1-3B, a unified generalist language model that simultaneously achieves strong agentic behavior, code generation, and general reasoning with only 3B parameters.…
Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity
Haihui Pan, Yuzhong Hong, Kaichen Zhang +4
In many large language model (LLM) alignment applications, users expect not only high-quality outputs but also substantial diversity. However, existing methods often face a fundame…
Measuring and Mitigating Post-hoc Rationalization in Reverse Chain-of-Thought Generation
Guangyue Peng, Zongchao Chen, Wen Luo +9
Reverse Chain-of-Thought Generation (RCG) synthesizes reasoning traces from query-answer pairs, but answer-visible generation can justify a pre-committed answer rather than derive…
NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents
Yang Song, Anoushka Vyas, Zirui Wei +3
We present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations using autonomous codin…
Reasoning's Razor: Reasoning Improves Accuracy but Can Hurt Recall at Critical Operating Points in Safety and Hallucination Detection
Atoosa Chegini, Hamid Kazemi, Garrett Souza +5
Reasoning has become a central paradigm for large language models (LLMs), consistently boosting accuracy across diverse benchmarks. Yet its suitability for precision-sensitive task…
gpt-oss-120b & gpt-oss-20b Model Card
OpenAI, :, Sandhini Agarwal +124
We present gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models that push the frontier of accuracy and inference cost. The models use an efficient mixture-of-expert trans…
Prompting is not Enough: Exploring Knowledge Integration and Controllable Generation
Tingjia Shen, Hao Wang, Chuan Qin +5
Open-domain question answering (OpenQA) represents a cornerstone in natural language processing (NLP), primarily focused on extracting answers from unstructured textual data. With…
PEACE: Empowering Geologic Map Holistic Understanding with MLLMs
Yangyu Huang, Tianyi Gao, Haoran Xu +8
Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface. These maps are indispens…
LLM4PR: Improving Post-Ranking in Search Engine with Large Language Models
Yang Yan, Yihao Wang, Chi Zhang +8
Alongside the rapid development of Large Language Models (LLMs), there has been a notable increase in efforts to integrate LLM techniques in information retrieval (IR) and search e…
Can a large language model be a gaslighter?
Wei Li, Luyao Zhu, Yang Song +3
Large language models (LLMs) have gained human trust due to their capabilities and helpfulness. However, this in turn may allow LLMs to affect users' mindsets by manipulating langu…
A Variational Approach to Unsupervised Sentiment Analysis
Ziqian Zeng, Wenxuan Zhou, Xin Liu +4
In this paper, we propose a variational approach to unsupervised sentiment analysis. Instead of using ground truth provided by domain experts, we use target-opinion word pairs as a…