57 citations · 147 across the 34 of their papers we have counts for
34 papers
Financial Numerical Prediction and Allocation as Token Generation
Xu Ouyang, Moontae Lee
Financial prediction typically relies on task-specific regression, ranking, or policy heads, separating the language model from the numerical object ultimately evaluated. We invest…
EXAONE 3.0 7.8B Instruction Tuned Language Model
Soyoung An, Kyunghoon Bae, Eunbi Choi +34
We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model size…
Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense
Siqi Shen, Lajanugen Logeswaran, Moontae Lee +3
Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations. However, their understanding of cultural commonsense re…
Small Language Models Need Strong Verifiers to Self-Correct Reasoning
Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran +4
Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated criti…
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
Sungik Choi, Hankook Lee, Honglak Lee +1
Novelty detection is a fundamental task of machine learning which aims to detect abnormal ( out-of-distribution (OOD)) samples. Since diffusion models have recently…
Code Models are Zero-shot Precondition Reasoners
Lajanugen Logeswaran, Sungryull Sohn, Yiwei Lyu +5
One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point. This work…