3 citations · 8 across the 25 of their papers we have counts for
7 papers · 1 filter
Correlation and Navigation in the Vocabulary Key Representation Space of Language Models
Letian Peng, Chenyang An, Jingbo Shang
Language model (LM) decoding is based on the next-token prediction (NTP) probability distribution. For neural LMs (e.g., Transformer-based), NTP distribution is essentially a softm…
Text Grafting: Near-Distribution Weak Supervision for Minority Classes in Text Classification
Letian Peng, Yi Gu, Chengyu Dong +2
For extremely weak-supervised text classification, pioneer research generates pseudo labels by mining texts similar to the class names from the raw corpus, which may end up with ve…
Incubating Text Classifiers Following User Instruction with Nothing but LLM
Letian Peng, Jingbo Shang
In this paper, we aim to generate text classification data given arbitrary class definitions (i.e., user instruction), so one can train a small text classifier without any human an…
Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing
Letian Peng, Jingbo Shang
Persona-driven role-playing (PRP) aims to build AI characters that can respond to user queries by faithfully sticking with all persona statements. Unfortunately, existing faithfuln…
Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving
Chenyang An, Zhibo Chen, Qihao Ye +6
Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof s…
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks
Letian Peng, Zilong Wang, Feng Yao +2
Information extraction (IE) is a fundamental area in natural language processing where prompting large language models (LLMs), even with in-context examples, cannot defeat small LM…
Answer is All You Need: Instruction-following Text Embedding via Answering the Question
Letian Peng, Yuwei Zhang, Zilong Wang +4
This work aims to build a text embedder that can capture characteristics of texts specified by user instructions. Despite its tremendous potential to deploy user-oriented embedding…